Methods of treating diseases

ABSTRACT

The present invention relates to computer-implemented methods and system for analyzing a biomarker which cycles in a subject. In some other aspects, the present invention relates to analyzing a biomarker which at least initially increases or decreases in amount in a subject following a treatment for a disease. In further aspects, the present invention relates to computer-implemented methods and systems for determining a preferred time to administer a therapy to treat a disease in a subject. The present invention also relates to computer program product to implement the methods. Further, the present invention relates to methods of determining the timing of treating a disease in a subject in which the immune system is cycling.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a national stage application under 35 U.S.C. 371 ofPCT Application No. PCT/AU2010/000649, having an international filingdate of May 27, 2010, which designated the United States, which PCTapplication claimed the benefit of U.S. Patent Application No.61/181,508, filed on May 27, 2009, the entire disclosure of each ofwhich are hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to computer-implemented methods and systemfor analysing a biomarker which cycles in a subject. In some otheraspects, the present invention relates to analysing a biomarker which atleast initially increases or decreases in amount in a subject followinga treatment for a disease. In further aspects, the present inventionrelates to computer-implemented methods and systems for determining apreferred time to administer a therapy to treat a disease in a subject.The present invention also relates to computer program product toimplement the methods. Further, the present invention relates to methodsof determining the timing of treating a disease in a subject in whichthe immune system is cycling.

BACKGROUND OF THE INVENTION Regulatory T cells

Studies have identified the existence of a naturally occurringpopulation of regulatory/suppressor T cells, which, upon in vitroTCR-mediated stimulation, suppress the proliferation of effector T cells(von Herrath and Harrison, 2003; Allan et al., 2008; Brusko et al.,2008; Vila et al., 2009). These cells are central to the control of Tcell homeostasis and in the modulation of immune responses toautoantigens, cancer cells, pathogens, and alloantigens.

In the periphery of young mice not prone to autoimmune diseases,regulatory T cells constitute a stable 10% of CD4⁺ T cells. Thisproportion appears to be reduced in mice genetically prone to autoimmunedisease such as diabetes (Salomon et al., 2000). Transfer of regulatoryT cells has been shown to prevent a wide range of experimentalautoimmune diseases, including diabetes, experimental autoimmuneencephalomyelitis, and colitis (Salomon et al., 2000; Wu et al., 2002;Kohm et al., 2002; Read et al., 2000). Furthermore, depletion ofregulatory T cells has been shown to exacerbate various experimentalautoimmune diseases, including collagen induced arthritis. In humans, ananalogous population of CD4⁺CD25⁺ regulatory T cells has been identifiedin the peripheral blood and the thymus (Jonuliet et al., 2001;Annunziato et al., 2002).

Autoimmune Diseases

Many autoimmune disorders arise when cells of specific tissues becomethe targets of T lymphocytes (for reviews see Santamaria, 2001; Vila etal., 2009). Much of what is currently known about effector pathways ofautoimmunity has been learned from spontaneous and experimental modelsof autoimmune disease. Type 1 diabetes mellitus (T1D) in non-obesediabetic (NOD) mice is a prototypic model of spontaneous, organ-specificautoimmunity. NOD mice spontaneously develop a form of autoimmunediabetes, closely resembling human T1D, that results from destruction ofthe pancreatic β-cells by T lymphocytes.

Studies of CD8+ T-cell-deficient NOD mice indicate that the initialβ-cell insult in T1D is effected by cytotoxic CD8+ T cells. Severaltransgenic models of T1D have shown that CD8+ T cells can readily killβ-cells expressing transgenic neo-antigens; however, little is knownabout the antigenic specificity or specificities of the CD8+ T cellsthat kill β-cells in NOD mice. Wong et al. (1999) have reported thatthere is a CD8+ T-cell subpopulation that recognizes an insulin-derivedpeptide in the islets of young NOD mice. Furthermore, immunopathologicalstudies of pancreata from human diabetic individuals and pancreasisograft recipients have suggested that destruction of β-cells in humanT1D may also be effected, at least in part, by CD8+ effector T cells(Bottazzo et al., 1985).

Experimental autoimmune encephalomyelitis (EAE) is a prototypicexperimental autoimmune disease that models human multiple sclerosis andthat develops in susceptible rodent strains after immunization withmyelin basic protein, proteolipid antigen or myelin oligodendrocyteprotein (MOG). Evidence suggests that CD8+ T cells have a role indisease progression and severity (reviewed by Goverman, 1999). Myelinbasic protein is processed in vivo by the MHC class I pathway, and aMOG-derived peptide activates encephalitogenic CD8+ T cells in vivo.There is also evidence for clonal expansions of CD8+ T cells in activemultiple-sclerosis lesions (Babbe et al., 2000).

Graft-Versus-Host Disease

Graft-versus-host disease is a multistep process. It has been shown thateffector T cells play the pivotal role in the induction of the disease.During the ‘induction phase’ the effector T cells see alloantigendisparities and then become activated and clonally expand (the‘expansion stage’). These T cells then release cytokines and possiblychemokines (for example macrophage inflammatory protein 1α), resultingin the recruitment of other cell types (macrophages, granulocytes,natural killer cells, etc.) in the ‘recruitment phase’. Finally, the Tcells and the other cell types mediate the pathology associated withgraft-versus-host disease (the ‘effector phase’) (for a review seeMurphy and Blazar, 1999).

There has been emphasis on delineating the effector mechanisms ofgraft-versus-host disease. T cells and other cells primarily mediatetheir effector functions through either FasL, perforin-granzyme-B orTNF. The use of knockout mice has demonstrated a pivotal role for eachof these pathways in the effector stage of graft-versus-host disease.FasL and perforin-granzyme-B appear critical for solid organ pathologywhereas TNF appears to mediate the wasting/weight loss associated withgraft-versus-host disease. TNF also appears to be induced, along withother cytokines, after conditioning (Hill et al., 1997)—demonstratingthat cytokines elicited by either the donor or the recipient affectgraft-versus-host disease. TNF-receptor knockout mice and the use ofanti-TNF antibodies have been shown to be protective ingraft-versus-host disease models (Speiser et al., 1997).

Cancer

In the past, attempts have been made to trigger the immune system tomount an efficient response against malignant cells. Despite significantand promising progress, such a response has yet to be fully attained andmany immune based therapies have proved disappointing.

Numerous studies using in vitro cellular assays demonstrate thatcytotoxic lymphocytes have the ability to kill tumour cells. The cancerpatient also has increased concentration of circulating immunecomplexes, indicating the immune system is active, particularly againstcertain tumour antigens. The level of these immune complexes canincrease with disease progression (Horvath et al., 1982; Aziz et al.,1998).

Regulatory T cells have been implicated in a subject's immune responseto cancer (North and Awwad, 1990; Gajewski et al., 2009). As most cancerantigens are actually produced by the patient they are considered as“self” by the immune system. Upon the presence, and/or increasedquantity, of tumour antigen the host's immune system mounts a responsecharacterized by the production of effector T cells which target cellsproducing the tumour antigen. However, in many instances these effectorT cells are recognized by the immune system as targeting the host's owncells, and hence a population of regulator T cells are produced todown-regulate the effector T cell population. Thus, the production ofthese regulator T cells limits the ability of the immune system toeffectively remove cancer cells.

Degenerative Diseases

Whilst degenerative diseases such as Alzheimer's disease are notclassically considered to be mediated by inflammation or the immunesystem, in some instances the immune system may play an important rolein the degenerative process. In addition, it has become clear that theimmune system itself may have beneficial effects in nervous systemdiseases considered degenerative. Immunotherapeutic approaches designedto induce a humoral immune response have recently been developed for thetreatment of Alzheimer's disease. In animal models, it has also beenshown that immunotherapy designed to induce a cellular immune responsemay be of benefit in central nervous system injury, although T cells mayhave either a beneficial or detrimental effect depending on the type ofT cell response induced (Monsonego and Weiner, 2003).

Infections

More recently, regulator T cells have been shown to be involved in asubject's immune response to a viral infection. WO 02/13828 describesthe production of regulator T cells during retroviral infection, andmethods of treating such infections by down-regulating the regulator Tcell population whilst maintaining the effector T cell population. SinceWO 02/13828 was filed there have been a large number of studies whichhave identified a role for regulatory T cells in the progression ofchronic retroviral infections. This includes studies on Friendretrovirus infection (Iwashiro et al., 2001), Feline immunodeficiencyvirus (Vahlenkamp et al., 2004), Simian immunodeficiency virus(Hryniewicz et al., 2006; Estes et al., 2007) and many studies on HIV(Weiss et al., 2004; Kinter et al., 2004; Lim et al., 2006; Nilsson etal., 2006; Kinter et al., 2007a; Kinter et al., 2007b; Lim et al., 2007;Cao et al., 2009). The role of regulatory T cells in the progression ofchronic retroviral infections has also been the subject of many recentreviews including those by Vahlenkamp et al. (2005), Belkaid and Rouse(2005), Rouse et al. (2006) and Dittmer (2004).

Treatment of Diseases Involving Effector and Regulator T Cells

Taking advantage of regulatory T cells has been complicated by aninability to expand and characterize this minor T cell subset, apopulation of cells reduced even further in autoimmune-prone animals andpatients. For instance, studies have suggested that it may be impossibleto reverse ongoing autoimmune diabetes due to the autoreactive T cellsbecoming resistant to suppression during the active phase of thedisease. Prior efforts to expand regulatory T cells ex vivo have notachieved clinically sufficient expansion, nor demonstrable in vivoefficacy. The low number of CD4+ CD25+ regulatory T cells, their anergicphenotype and diverse antigen specificity present major challenges toharnessing this potent tolerogenic population to treat autoimmunediseases and transplant rejection.

WO 03/070270 describes the use of acute phase inflammatory markers inregimes for the effective treatment of HIV. These methods rely on atleast partially “resetting” the immune system by a treatment such asHAART followed by the analysis of acute phase inflammatory proteins asmarkers for effector and regulator T cell expansion. The emergence ofacute phase inflammatory proteins appears to be linked to effector Tcell expansion, which occurs before regulator T cell expansion, and thusthe patient can be treated with a suitable agent which allows theeffector T cell population to be maintained whilst destroying,preventing the production of, or reducing the activity of, regulator Tcells. In essence, upon withdrawal of HAART treatment it was consideredthat the patient's immune system would treat the re-emerging HIVparticles as a new infection, and hence a new population of effector Tcells would be produced.

Similar to WO 03/070270, WO 03/068257 relates to at least partiallyresetting the immune system, however, in this instance in the context ofthe treatment of cancer. Again, the treatment is focussed on the initialre-emergence of effector T cells following a reduction in tumour loadthrough techniques such as surgery or the administration ofanti-proliferative drugs.

Following from the advances described in WO 02/13828, WO 03/070270 andWO 03/068257, it was later surprisingly found that the immune system iscycling in many chronic disease states such as cancer, retroviralinfections (both in WO 05/040816) and autoimmune diseases (WO06/026821). Thus, it is not essential that the disease state be “reset”to be able to target the regulator T cells to effectively treat thediseases involving regulator T cells such as cancer. Despite the groundbreaking advances described in WO 05/040816 and WO 06/026821, variationsbetween individuals, variations in sample testing, and the complexity ofthe disease states make it difficult to manage the data to allow theroutine targetting of the desired cell type on the first attempt. Thus,there is a need for mechanisms to increase the likelihood of effectivelytreating a disease in which the immune system is cycling in the initialattempt(s).

SUMMARY OF THE INVENTION

According to a first aspect, the present invention provides acomputer-implemented method for analysing a biomarker which cycles in asubject with a disease, the method comprising estimating a periodicityof the cycling of the biomarker based on measurements of the biomarker.

The method may further comprise determining from the estimatedperiodicity of the cycling a preferred time in the future to administera therapy to treat the disease in the subject. The measurements of thebiomarker may be received via a communications network, or retrievedfrom a remote or local storage.

Estimating the periodicity may comprise obtaining a best-fit curve tothe measurements in accordance with a model of the cycling of thebiomarker. The model of the periodicity of the cycling biomarker may insome embodiments be of the form:log(marker_(i))=cos(2π×((time_(i)−offset)/(period))×amplitude+mean+ε_(i)where marker_(i) is a measurement taken at time_(i), and parametersoffset, period, amplitude, mean and model error or residual ε_(i) areunknown. Preferably, the model errors are normally distributed with zeromean and constant but unknown variance, or from one of the family of tdistributions with degrees of freedom as determined by the model fittingmechanism.

For example, where the measurements are CRP measurements obtained with atime specificity of one day, the model may be of the form:

${\log\left( {CRP}_{i} \right)} = {{{\cos\left( {2\pi \times \left( \frac{{day}_{i} - {offset}}{period} \right)} \right)} \times {amplitude}} + {mean} + \varepsilon_{i}}$where CRP_(i) is a CRP measurement taken at day_(i), and parametersoffset, period, amplitude, mean and model error or residual ε_(i) areunknown.

The method may further comprise projecting the obtained best-fit curveinto the future to determine the preferred time in the future toadminister the therapy. The best-fit curve may be a fit to a harmonicmodel of the cycling of the biomarker. In this case, obtaining thebest-fit curve further comprises fitting the harmonic model to themeasurements multiple times using different initial conditions for eachfit. For example where the harmonic model includes the period as aparameter, the initial value of the period may be altered for each fitso as to generate different best-fit results. In one embodiment,different initial estimates of cycle period, such as but not limited to,about 3 days, about 5 days, about 7 days or about 9 days. Where thecurves so generated differ, reduced confidence in the best fit resultscan be inferred. This may include at least two, and preferably threecurves under different regression models for the single set of data. Insuch embodiments a similarity or disparity of the best fit result ofeach regression model is preferably used to generate a confidencemeasure to indicate a confidence in the model having the highestlog-likelihood

Thus, in another embodiment, the method further comprises generating aconfidence measure by comparing a similarity in a fit result produced byeach fitting. Furthermore, in recognition of the difficulty ofextracting a periodic characteristic from a small number ofmeasurements, such as seven daily noisy measurements, such embodimentsof the present invention provide not only a method of analysing suchdata in a manner to give an estimated best fit periodic curve, but alsoprovide an indication of the confidence in the estimate.

Obtaining a best-fit curve may further comprise imposing a boxconstraint on at least some of parameters of the model to guideoptimisation to biologically realistic regions. In some embodiments, anoptimisation algorithm is repeatedly applied using altered levels ofdegrees of freedom, to allow for differing tolerance to data outliers. Afitted model with the highest log-likelihood may then be chosen for eachinitial condition for comparison to a fitted model obtained using analternative nominated initial condition. In this case, close agreementbetween the chosen fitted models may be taken to improve a confidence inthe estimate of the underlying cycle.

A variance-stabilizing transformation of the measurements may bedetermined prior to estimating the periodicity based on the transformedmeasurements. In taking a variance-stabilizing transformation of themeasurements, the noisy nature of the biomarker measurements and thenon-constant amplitude in the cycling biomarkers are considered.

Any suitable variance-stabilizing transformation of measurements may beused, such as logarithm of the measurements. The transformation takenmay be the identity, the square root, the natural logarithm, thelogarithm to base 10, any other transformation from the ladder ofpowers, or the like. The transformation will preferably be the naturallogarithm and the exposition that follows will assume that this has beenperformed. Noting that at least some biomarkers, for example c-reactiveprotein (CRP) levels, are measured as a concentration, negativemeasurements are not possible so that the variance-stabilizingtransformation can always be taken in such embodiments.

Determining the preferred time to administer the therapy may furthercomprise comparing the measurements of the subject with a database ofmeasurements of the biomarker obtained from other subjects who had thedisease and have been treated with a therapy. The therapy used to treatthe “other subjects” may be the same or different to that which isplanned to be administered to the subject, preferably the same.Furthermore, not all the “other subjects” may have been treated with thesame therapy, for example, the exact same antiproliferative drug.

The measurements may also be for two or more different biomarkers whichare cycling in the subject, in which case the method comprisesdetermining the preferred timing of administration by reference to twoor more sets of measurements. This may provide improved accuracy ofestimation of immune system cycling as compared to embodiments whichrely on measurements of a single biomarker. Alternatively oradditionally, two or more different types of measurements are taken forthe same biomarker. For example, daily or sub-daily measurements of anacute phase marker can be taken from a finger prick sample using ahand-held point of care monitoring device coupled with and calibrated byless frequent but more precise measurements obtained by detailed sampleexamination such as is provided by a professional laboratory.

The model may have a heavy tail to allow for noisy nature of themeasurements, such as by applying non-linear regression using maximumlikelihood and assuming a heavier-tailed error distribution. In thisembodiment, T regression replaces the assumption of the normaldistribution for the errors with the t distribution withstatistically-determined degrees of freedom. The degrees of freedomcorrelate to the weight of the tail (more degrees gives lighter tail);heavier tails provide greater robustness to outlier values.

In another embodiment, the use of an error distribution is selected toprovide robustness against extreme values, for example, the normal, thefamily of t distributions, the Cauchy, the gamma, the Weibull, and theJohnson S families, preferably the t family.

In cases where a periodic characteristic of the measurements cannot bedetermined or can only be estimated with low confidence, the method andalgorithm preferably further provides for a recommendation that furthersamples be analysed. Preferably also provided is a recommendation forthe preferred timing of measurements to help ensure the furthermonitoring provides sufficient data to characterize the cycling, such asthe times that the several models that have been produced are at theirfurthest points from one another. Alternatively, an output indication issent to a skilled operator to visually analyse the data and determinewhether a preferred timing can be estimated through human interventionor whether the recommendation that further samples be analysed is sentto the end user.

In some embodiments, the method is carried out on-site at a testingfacility at which the measurements are obtained from a diseased subject.In other embodiments, the method is carried out using a hand-held,generally point-of-care device, which is able to measure the biomarker,for example, a device which can measure acute phase inflammatory markerlevels from a drop of blood obtained from a finger-prick. In alternativeembodiments, the method is conducted at a central location remote from atesting facility.

Embodiments of the invention preferably further recognise that theobtained biomarker measurements are likely to constitute a sparse sampleset, due to the difficulty in obtaining such measurements less thanabout daily from the diseased subject. In a further embodiment, theactual time the therapy was administered, further measurements of thebiomarker following administration and/or the outcome of therapy areanalysed to allow further refinement of the determination of thepreferred time to administer a specific therapy, to a subject whoseimmune system is cycling in a particular way and/or to take into accountany other factors relevant to the determination of the preferred timefor a given set of circumstances.

According to another aspect, the present invention provides acomputer-implemented method for analysing a biomarker which cycles in asubject with a disease, the method comprising:

i) sending measurements of the biomarker to a computing device, whereinthe computing device is operable to estimate a periodicity of thecycling of the biomarker based on the measurements; and

ii) receiving the estimated periodicity of the cycling from thecomputing device.

According to a further aspect, the present invention provides a computersystem for analysing a biomarker which cycles in a subject with adisease, the system comprising a computing device operable to estimate aperiodicity of the cycling of the biomarker in the subject based onmeasurements of the biomarker.

The computing device may also be operable to determine from theestimated periodicity of the cycling a preferred time to administer atherapy to treat a disease in the subject.

The measurements may be received via a communications network from aplurality of distributed testing locations, the measurements relating tothe cycling of a biomarker of each of a plurality of diseased subjects.

According to a further aspect, the present invention provides a computerprogram product comprising computer program code means to cause acomputer to implement a method for determining a preferred timing ofadministration of a therapy in accordance with a method of theinvention.

According to another aspect, the present invention provides an apparatuscomprising a data processing means which is arranged to determine apreferred timing of administration of a therapy in accordance with amethod of the invention.

Instead of relying on the analysis of the cycling of the immune systemto treat the disease, the immune system can at least be partially“reset” and the emerging T cell population of interest (effector orregulator depending on the disease) can be targeted at the appropriatetime. Thus, in yet another aspect, the present invention provides acomputer-implemented method for analysing a biomarker which at leastinitially increases or decreases in amount in a subject following atreatment for a disease, the method comprising estimating the timingand/or rate of the at least initial increase or decrease in amount ofthe biomarker based on measurements of the biomarker, the measurementshaving been at least obtained after the treatment.

The method may further comprise determining from the estimated timingand/or rate of increase or decrease a preferred time in the future toadminister the therapy. The measurements of the biomarker may bereceived via a communications network, or retrieved from a remote orlocal storage.

Before and after said treatment the amount of the biomarker may becycling in the subject, in which case the increase or decrease reflectsthe beginning of the first cycle following said treatment. Thus, thelevels of the biomarker is typically cycling in the subject with thedisease and the treatment disrupts and resets the cycling. Furthermore,this means that this aspect, and related aspects, are practicedimmediately after the treatment, such as within about 28 days, or withinabout 21 days, or within about 14 days.

Examples of biomarkers which increase after the treatment are, but notlimited to, effector T cells, regulator T cells (after the effector Tcells) and positive acute phase inflammatory markers. Examples ofbiomarkers which decrease after treatment are, but not limited to,cancer antigen markers in subjects with cancer, and viral load insubjects with a viral infection such as HIV.

The “treatment” and the “therapy” in this, and related aspects, can bethe same or different. Thus, this, and related aspects, can beconsidered as providing a first therapy to at least partially reset theimmune system and a second therapy to effectively treat the disease bytargeting the T cell population of interest.

Estimating the timing and/or rate may comprise obtaining a best-fitcurve to the measurements in accordance with a model of the cycling ofthe biomarker to estimate the timing and rate of the at least initialincrease or decrease in amount of the biomarker. Obtaining the best-fitcurve may further comprise imposing a box constraint on at least someparameters of the model to guide optimisation to biologically realisticregions. The method may further comprise projecting the obtainedbest-fit curve into the future to estimate the preferred time toadminister the therapy.

The method may further comprise determining a variance-stabilizingtransformation of the measurements prior to estimating the timing andrate. The variance-stabilizing transformation of measurements may belogarithm of the measurements. Other suitable transformations include

In a further aspect, the present invention relates to acomputer-implemented method for analysing a biomarker which at leastinitially increases or decreases in amount in a subject following atreatment for a disease, the method comprising:

i) sending measurements of the biomarker in the subject to a computingdevice, wherein the measurements are at least obtained after saidtreatment, and wherein the computing device is operable to estimate thetiming and/or rate of the at least initial increase or decrease inamount of the biomarker based on the measurements; and

ii) receiving the estimated timing and/or rate of the at least initialincrease or decrease in amount of the biomarker.

In a further aspect, the present invention relates to a computer programproduct comprising computer program code means to cause a computer toimplement a method for analysing a biomarker which at least initiallyincreases or decreases in amount in a subject following a treatment fora disease.

In a further aspect, the present invention relates to a computer systemfor analysing a biomarker which at least initially increases ordecreases in amount in a subject following a treatment for a disease,the system comprising a computing device operable to estimate the timingand/or rate of the at least initial increase or decrease in amount ofthe biomarker based on measurements of the biomarker, the measurementsbeing at least obtained after the treatment.

In a further aspect, the present invention relates to acomputer-implemented method for determining a preferred time toadminister a therapy to treat a disease in a subject, the methodcomprising:

i) based on measurements of a biomarker which cycles in the subject,estimating a periodicity of the cycling of the biomarker; and

ii) determining from the estimated periodicity of the cycling apreferred time in the future to administer the therapy to treat thedisease in the subject.

In a further aspect, the present invention relates to acomputer-implemented method for determining a preferred time toadminister a therapy to treat a disease in a subject, the methodcomprising:

i) based on measurements of a biomarker which at least initiallyincreases or decreases in amount in the subject following a treatmentfor the disease, estimating a timing and/or rate of the at least initialincrease or decrease, wherein the measurements are at least obtainedafter the treatment; and

ii) determining from the estimated timing and/or rate of increase ordecrease a preferred time in the future to administer the therapy.

In a further aspect, the present invention relates to a computer systemfor determining a preferred time to administer a therapy to treat adisease in a subject, the system comprising a computing device operableto:

i) based on measurements of a biomarker which cycles in the subject,estimate a periodicity of the cycling of the biomarker; and

ii) determine from the estimated periodicity of the cycling a preferredtime in the future to administer the therapy to treat the disease in thesubject.

In a further aspect, the present invention relates to a computer systemfor determining a preferred time to administer a therapy to treat adisease in a subject, the system comprising a computing device operableto:

i) based on measurements of a biomarker which at least initiallyincreases or decreases in amount in the subject following a treatmentfor the disease, estimate a timing and/or rate of the at least initialincrease or decrease, wherein the measurements are at least obtainedafter the treatment; and

ii) determine from the estimated timing and/or rate of increase ordecrease a preferred time in the future to administer the therapy.

In a further aspect, the present invention relates to a computer programproduct comprising computer program code means to cause a computerimplement a method for determining a preferred time to administer atherapy to treat a disease in a subject.

With regard to cycling levels of an acute phase inflammatory marker, thepresent inventors have identified a particularly preferred timing ofadministration of the therapy to treat the disease. Thus, in a furtheraspect the present invention provides a method of treating a disease ina subject in which the immune system is cycling, the method comprising;

i) monitoring the subject to determine the periodicity of the cycling ofan acute phase inflammatory marker in the subject, and

ii) administering the therapy whilst the level of the acute phaseinflammatory marker is increasing, between half way between a minimum inthe cycle and a maximum in the cycle, but before the levels of the acutephase inflammatory marker have peaked,

wherein the disease is characterized by the production of regulator Tcells.

An example of this preferred timing of administration is shownschematically in FIG. 19.

Preferably, the disease characterized by the production of regulator Tcells is selected from, but not limited to, cancer, an infection and adegenerative disease.

The infection can be caused by any type of infectious agent such as, butnot limited to, a virus, bacteria, protozoa, nematode, prion, or fungus.Preferably, the infectious agent causes chronic persistent infectioncharacterized by the patient's immune system not being able to eliminatethe infectious agent. Examples of infectious agents which cause chronicpersistent infection are viruses such as HIV, the Hepatitis B virus andthe Hepatitis C virus.

In another aspect the present invention provides a method of treating adisease in a subject in which the immune system is cycling, the methodcomprising;

i) monitoring the subject to determine the periodicity of the cycling ofan acute phase inflammatory marker in the subject, and

ii) administering the therapy between just before and just after theacute phase inflammatory marker has reached its lowest point in thecycle,

wherein the disease is characterized by the production of effector Tcells.

Preferably, the disease characterized by the production of effector Tcells is selected from, but not limited to, an autoimmune disease ortransplant rejection.

In a preferred embodiment of the two above aspects, it is preferred thata second biomarker which is out of phase with the cycling of the acutephase inflammatory marker is also monitored and used to more accuratelydetermine when to administer the therapy. Examples of biomarkers whichcycle out of phase with acute phase inflammatory markers include, butare not limited to, TGFβ and IL-10.

With regard to the two above aspects, preferably the acute phaseinflammatory marker is a positive acute phase inflammatory marker.Examples of positive acute phase inflammatory markers include, but arenot limited to, c-reactive protein (CRP), serum amyloid A (SAA), serumamyloid P component, complement proteins such as C2, C3, C4, C5, C9, B,C1 inhibitor and C4 binding protein, fibrinogen, von Willebrand factor,α1-antitrypsin, α1-antichymotrypsin, α2-antiplasmin, heparin cofactorII, plasminogen activator inhibitor I, haptoglobin, haemopexin,ceruloplasmin, manganese superoxide dismutase, α1-acid glycoprotein,haeme oxygenase, mannose-binding protein, leukocyte protein I,lipoporotein (a), lipopolysaccharide-binding protein, and interleukinssuch as IL-1, IL-2, IL-6, IL-10 and receptors thereof. Preferably, thepositive acute phase inflammatory marker is CRP or SAA, more preferablyCRP.

Due to the complexities described below, the present inventors havedevised a treatment regime which optimizes the possibility ofeffectively treating the disease. This procedure relies on slightlyoverestimating the optimal time of administration of the therapy, andthen backing into the cycle with recurrent treatments over a number ofcycles. Accordingly, in a further aspect the present invention providesa method of treating a disease in a subject in which the immune systemis cycling, the method comprising

i) monitoring the subject to determine the periodicity of the cycling ofa biomarker which cycles in the disease,

ii) predicting the optimal time in the cycle to administer a therapy totreat the disease,

iii) administering a first therapy at a time selected after thepredicted optimal time,

iv) administering a second therapy in the next cycle following step iii)at a time selected earlier in the cycle than the first therapy, andoptionally

v) administering a third therapy in the next cycle following step iv) ata time selected earlier in the cycle than the second therapy.

In one embodiment, the first therapy is administered between about 12hours to about 48 hours after the predicted optimal time. In anotherembodiment, the second therapy is administered between about 12 hoursand about 24 hours earlier in the cycle than the first therapy. In afurther embodiment, the third therapy is administered between about 12hours and about 24 hours earlier in the cycle than the second therapy.The treatment can be continued with further therapies administered insubsequent cycle between about 12 hours and about 24 hours earlier thanthe previous cycle. However, it is likely that two, and at most three,administrations of the therapy will be sufficient to treat the disease.

The first, second, third, etc therapies can be the same or differenttherapies, but preferably the same.

Also provided is the use of a therapy for the manufacture of amedicament for treating a disease in a subject in which the immunesystem is cycling, wherein the subject is monitored to determine theperiodicity of cycling of an acute phase inflammatory marker in thesubject, and the therapy is to be administered whilst the level of theacute phase inflammatory marker is increasing, between half way betweena minimum in the cycle and a maximum in the cycle, but before the levelsof the acute phase inflammatory marker have peaked, wherein the diseaseis characterized by the production of regulator T cells.

Also provided is the use of a therapy for the manufacture of amedicament for treating a disease in a subject in which the immunesystem is cycling, wherein the subject is monitored to determine theperiodicity of cycling of an acute phase inflammatory marker in thesubject, and the therapy is to be administered between just before andjust after the acute phase inflammatory marker has reached its lowestpoint in the cycle, wherein the disease is characterized by theproduction of effector T cells.

Also provided is the use of a therapy for the manufacture of amedicament for treating a disease in a subject in which the immunesystem is cycling, wherein the subject is monitored to determine theperiodicity of the cycling of a biomarker which cycles in the disease,and the therapy is to be sequentially administered

i) at a time selected after a predicted optimal time to administer thetherapy followed by

ii) at a time selected earlier in the cycle than i) and optionallyfollowed by

iii) at a time selected earlier in the cycle than ii).

Unless specified otherwise, the biomarker to be analysed is any cell ormolecule, the levels of which are cycling in the diseased subject.Examples of such biomarkers include, but are not limited to, regulator Tcells, effector T cells, molecules associated with the disease, and/orimmune system markers.

Preferably, the immune system marker reflects the number and/or activityof regulator T cells, and/or the number and/or activity of effector Tcells. In a preferred embodiment, the immune system marker (biomarker)is an acute phase inflammatory protein.

In an embodiment, the regulator T cells are CD4+CD8− T cells.

In another embodiment, the effector T cells are CD8+CD4− T cells.

In a further embodiment, the molecule associated with the disease is anantigen, or nucleic acid encoding therefore, produced by a cancer cellor an infectious agent.

It will be appreciated by the skilled person that diseases such ascancer, autoimmunity and AIDS have a complex effect on the patient.Furthermore, natural variations between individuals linked to factorssuch as their genotype, nutrition, fitness, previous and current diseasestatus, all influence how a given individual responds to a diseasestate. Thus, whilst in most cases the cycle will be somewhere between 3and 15 days (often depending on the biomarker being analysed), in someindividuals this may be slightly shorter or longer. In addition, likethe menstrual cycle, the length of the cycle may vary slightly within anindividual due to natural variation and/or environmental factors. Thus,individual variation may at least be encountered with regard to, forexample, i) the length (periodicity) of the cycle, ii) the absolutenumbers of effector or regulator T cells during the cycle, or iii) thelevels of acute phase inflammatory markers during the cycle. Suchvariation may be exaggerated in patients with advanced disease, wherethe patient's immune system has been challenged for a considerablelength of time.

As result, it will most likely be desirable to monitor the subject for asufficient length of time to ensure that the dynamics of biomarkercycling within a particular subject is understood. Preferably, thesubject is monitored/measured for a period of at least 7 days, morepreferably at least 14 days, more preferably at least 21 days, morepreferably at least 28 days, more preferably at least 35 days, morepreferably at least 42 days, and even more preferably at least 49 days.

Furthermore, it is preferred that the subject is monitored as frequentlyas possible to ensure biomarker cycling within a given subject issuitably characterized. Naturally this will ensure that the therapy isadministered at the appropriate time and that any small variation in thebiomarker is not misinterpreted. Preferably, the subject is monitored atleast every 3 days, more preferably at least every 2 days, and mostpreferably at least every day. Monitoring may occur more frequently, forinstance every 12 hours, when the cycling is reaching a stage where itis likely that the timing would be appropriate to administer thetherapy. Thus, it is preferred that serial measurements with a definedfrequency have been taken, however, the invention nonetheless allows theuse of infrequent measurements as long as there is enough representativedata to make a prediction as to when to administer the therapy.

A further complicating factor will be if the subject has recentlyacquired a disease or trauma unrelated to that being treated. Forexample, a subject being treated for a HIV infection may also contractthe common flu virus. The presence of the flu virus will result in, forexample, an increase in acute phase inflammatory markers independent ofthe cycling of these markers which is occurring due to the HIVinfection. Accordingly, it is desirable to monitor the subject for anyfactors which may result in elevated levels of, for example, acute phaseinflammatory markers to ensure that the factor being monitored trulyreflects biomarker cycling resulting from the disease being treated.

As outlined above, in an embodiment numerous biomarkers are monitored atthe same time. This is because, due to the factors describe above, it isunlikely that each biomarker will have a perfect cycle profile within agiven period, particularly over a number of cycles, to routinely providea clear indication of the appropriate time to administer the therapy.Whilst the analysis of numerous factors of a long period may be costly,and may be of at least some inconvenience to the subject, diseases suchas cancer and AIDS are life threatening. Hence it is worthwhileunderstanding as much as possible regarding biomarker cycling in a givensubject before the subject is treated.

When the disease is characterized by the production of regulator T cellsit is particularly preferred the therapy inhibits the production of,limits the function of, and/or destroys, regulator T cells. Morepreferably, the therapy is selected from the group consisting ofanti-cancer drugs such as anti-proliferative drugs, a vaccine,radiation, dsRNA and antibodies which inhibit the production and/oractivity of regulator T cells. Preferably, the anti-proliferative drugis selected from the group consisting of, but not limited to, taxol,vincristine, vinblastine, temozolomide and anhydro vinblastine.

Examples of preferred antibodies for treating a disease characterized bythe production of regulator T cells include, but are not limited to,anti-CD4+, anti-CTLA-4 (cytotoxic lymphocyte-associated antigen-4),anti-GITR (glucocorticoid-induced tumour necrosis factor receptor),anti-CD28 and anti-CD25.

When the disease is characterized by the production of effector T cellsit is particularly preferred the therapy inhibits the production of,limits the function of, and/or destroys, effector T cells. Morepreferably, the therapy is selected from the group consisting ofanti-cancer drugs such as anti-proliferative drugs, a vaccine,radiation, dsRNA and antibodies which inhibit the production and/oractivity of effector T cells. An example of an antibody for treating adisease characterized by the production of effector T cells is ananti-CD8+ antibody.

As will be apparent, preferred features and characteristics of oneaspect of the invention are applicable to many other aspects of theinvention.

Throughout this specification the word “comprise”, or variations such as“comprises” or “comprising”, will be understood to imply the inclusionof a stated element, integer or step, or group of elements, integers orsteps, but not the exclusion of any other element, integer or step, orgroup of elements, integers or steps.

The invention is hereinafter described by way of the followingnon-limiting examples and with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1: Illustrates a distributed system for obtaining cyclic biomarkermeasurements, centrally processing such measurements, and determining asuitable future time for administration of a therapy.

FIG. 2: A flowchart of steps performable by a measurement device at atesting location in communication with a central computing device via awide area communications network in a first application.

FIG. 3: A flowchart of steps performable by a central computing devicein communication with plural measurement devices via a wide areacommunications network in a first application.

FIG. 4: A flowchart of steps performable by a measurement device at atesting location in communication with a central computing device via awide area communications network in a second application.

FIG. 5: A flowchart of steps performable by a central computing devicein communication with plural measurement devices via a wide areacommunications network in a second application.

FIG. 6: Illustrates a general-purpose computing device that may be usedin an exemplary system for implementing the invention.

FIG. 7: CRP cycling in Patient 1 from the clinical study.

FIG. 8: CRP cycling in Patient 2 from the clinical study.

FIG. 9: CRP cycling in Patient 3 from the clinical study.

FIG. 10: CRP cycling in Patient 4 from the clinical study.

FIG. 11: CRP cycling in Patient 5 from the clinical study.

FIG. 12: CRP cycling in Patient 6 from the clinical study.

FIG. 13: CRP cycling in Patient 7 from the clinical study.

FIG. 14: CRP cycling in Patient 8 from the clinical study.

FIG. 15: CRP cycling in Patient 9 from the clinical study.

FIG. 16: CRP cycling in Patient 10 from the clinical study.

FIG. 17: CRP cycling in Patient 11 from the clinical study.

FIG. 18: CRP cycling in Patient 12 from the clinical study.

FIG. 19: Overview of predicted patient treatment times. The predictedoptimal time of administration based on the pilot study is highlighted.

FIG. 20: Example report for a randomly-generated patient. Thislow-variability patient has six measures, each spaced two days apart.

FIG. 21: Example report for a randomly-generated patient. Thislow-variability patient has six measures, each spaced one day apart.

FIG. 22: Example report for a randomly-generated patient. Thislow-variability patient has ten measures, each spaced one day apart.

FIG. 23: Example report for a randomly-generated patient. Thislow-variability patient has ten measures across 14 days.

FIG. 24: Example report for a randomly-generated patient. Thislow-variability patient has fifteen measures, each spaced one day apart.

FIG. 25: Example report for a randomly-generated patient. Thishigh-variability patient has six measures, each spaced two days apart.

FIG. 26: Example report for a randomly-generated patient. Thishigh-variability patient has six measures, each spaced one day apart.

FIG. 27: Example report for a randomly-generated patient. Thishigh-variability patient has ten measures, each spaced one day apart.

FIG. 28: Example report for a randomly-generated patient. Thishigh-variability patient has ten measures across 14 days.

FIG. 29: Example report for a randomly-generated patient. Thishigh-variability patient has fifteen measures, each spaced one dayapart.

FIG. 30: Summary plot of the interval lengths of the simulations. Thenominal rate is 0.95. CV refers to the patient's underlying variability;Symmetry differentiates between those designs that are (S)ymmetric,those that have a preponderance of measurement (L)ate, or with morepoints allocated to (B)oth tails; N is the number of measurements, andSpan refers to the number of days for the full regime. The intervallength increases with increasing underlying variability, and seemsunaffected by measurement symmetry, span, and the number ofmeasurements. However, these results should be interpreted in the lightof those presented in FIG. 31.

FIG. 31: Summary plot of the interval coverage rates of the simulations.The nominal rate is 0.95. CV refers to the patient's underlyingvariability; Symmetry differentiates between those designs that are(S)ymmetric, those that have a preponderance of measurement (L)ate, orwith more points allocated to (B)oth tails; N is the number ofmeasurements, and Span refers to the number of days for the full regime.Coverage increases with increasing sample size, seems largely unaffectedby the span of the measurement period, symmetry and underlyingvariability.

DETAILED DESCRIPTION OF THE INVENTION General Techniques and Definitions

Unless specifically defined otherwise, all technical and scientificterms used herein shall be taken to have the same meaning as commonlyunderstood by one of ordinary skill in the art (e.g., in cell culture,molecular genetics, cancer therapy, immunology, immunohistochemistry,protein chemistry, and biochemistry).

Unless otherwise indicated, the recombinant protein, cell culture, andimmunological techniques utilized in the present invention are standardprocedures, well known to those skilled in the art. Such techniques aredescribed and explained throughout the literature in sources such as, J.Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons(1984), J. Sambrook et al., Molecular Cloning: A Laboratory Manual, ColdSpring Harbour Laboratory Press (1989), T. A. Brown (editor), EssentialMolecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press(1991), D. M. Glover and B. D. Hames (editors), DNA Cloning: A PracticalApproach, Volumes 1-4, IRL Press (1995 and 1996), and F. M. Ausubel etal. (editors), Current Protocols in Molecular Biology, Greene Pub.Associates and Wiley-Interscience (1988, including all updates untilpresent), Ed Harlow and David Lane (editors) Antibodies: A LaboratoryManual, Cold Spring Harbour Laboratory, (1988), and J. E. Coligan et al.(editors) Current Protocols in Immunology, John Wiley & Sons (includingall updates until present), and are incorporated herein by reference.

As used herein the terms “treating”, “treat” or “treatment” includeadministering a therapeutically effective amount of a therapy sufficientto reduce or eliminate at least one symptom of the disease. In anembodiment, these terms are used to indicate that the methods of theinvention increase the length of progression free survival of thesubject from the disease when compared to an untreated patient, and/orthe methods of the invention increase the average length of progressionfree survival of a group of subjects from the disease when compared tothe average from a group which have been randomly treated with thetherapy.

As used herein, “cycling” or “cycle” or variations thereof refers to arepetitive (persistent) oscillation of a biomarker (cell number,activity of, marker of disease, immune system marker etc.), wherein thebiomarker changes periodically from a maximum to a minimum for a givenlength of time which is typically about 3 days to about 15 days, moretypically about 7 days to about 14 days, depending on the biomarker.Furthermore, as used herein the term “the periodicity of the cycling” orvariations thereof refers to the length of time of one wave of the cyclefrom a given point to when the levels of the biomarker return to thecorresponding point in the next wave.

As used herein, the term “determining a preferred timing ofadministration of a therapy” or variations thereof refers to theanalysis of biomarker (immune system) cycling, or the timing and/or rateof the at least initial increase or decrease in amount of the biomarkerfollowing “resetting” the immune system, to predict when the therapyshould be administered to increase the chances the disease will beeffectively treated.

As used herein, the term “predicting the optimal time in the cycle”refers to the best estimation of when the therapy should be administeredto target the clonal expansion of the relevant cells based on themonitoring data.

The term “immune system marker” generally refers to any molecule orfactor which provides an indication of the state and/or activity of theimmune system. These markers may be directly linked to the activityand/or production of regulator and/or effector T cells, and/or mayprovide a more general indication of the overall response of the immunesystem to an antigen. Examples of a suitable immune system markerinclude acute phase inflammatory markers such as c-reactive protein andserum amyloid A. Another example of an immune system marker areindicators of cellular destruction such as, but not limited to,cholesterol and β-2-microglobulin in serum. Cholesterol andβ-2-microglobulin are integral components of cellular membranes. Inparticular, β-2-microglobulin is the accessory molecule to the MajorHistocompatabilty Class I or MHC-I receptor. Consequently, with thecycling of the anti-disease immune response together with target celldestruction, the serum levels in diseased patients of these twomolecules is often elevated. Thus, oscillations in indicators ofcellular destruction, such as cholesterol and β-2-microglobulin, mayalso prove useful in determining the beginning or end of the immuneresponse cycle. Another example of an immune system marker is bodytemperature, however, in this instance the patient is monitored directlywithout the need to obtain a sample.

As used herein, “out of phase” refers to two different biomarkerspeaking at different, for example opposite, times during immune systemcycling. More specifically, when one biomarker has peaked in the cycle,the other biomarker is about at its lowest point, and vice versa.

“Effector T cells” include, but are not necessarily limited to, the Tcell population known as CD8+ cells.

“Regulator T cells” include, but are not necessarily limited to, asubpopulation of CD4+ T cells. Such cells may also be referred to in theart as “suppressor cells” or “regulatory T cells”. Regulator T cells mayeither act directly on effector T cells or may assert their affects uponeffector T cells through other mechanisms.

CD4+ cells express the marker known in the art as CD4. Typically, theterm “CD4+ T cells” as used herein does not refer to cells which alsoexpress CD8. However, this term can include T cells which also expressother antigenic markers such as CD25.

As used herein, the term “inhibits the production of, limits thefunction of, and/or destroys” when referring to the exposure of the“effector T cells” to the therapy means that the number, and/oractivity, of effector T cells is down-regulated by the therapy. Mostpreferably, the number, and/or activity, of effector T cells iscompletely eradicated by the therapy.

As used herein, the term “inhibits the production of, limits thefunction of, and/or destroys” when referring to the exposure of the“regulator T cells” to the therapy means that the number, and/oractivity, of regulator T cells is down-regulated by the therapy. Mostpreferably, the number, and/or activity, of regulator T cells iscompletely eradicated by the therapy.

As used herein the term “disease characterized by the production ofregulator T cells” refers to any condition wherein the number oractivity of regulator T cells plays a role in prolonging the diseasestate. Examples of such disease include, but are not limited to, cancer,degenerative diseases and infection especially chronic persistentinfections.

As is known in the art, a cancer is generally considered as uncontrolledcell growth. The methods of the present invention can be used to treatany cancer including, but not limited to, carcinoma, lymphoma, blastoma,sarcoma, and leukemia. More particular examples of such cancers includebreast cancer, prostate cancer, colon cancer, squamous cell cancer,small-cell lung cancer, non-small cell lung cancer, ovarian cancer,cervical cancer, gastrointestinal cancer, pancreatic cancer,glioblastoma, liver cancer, bladder cancer, hepatoma, colorectal cancer,uterine cervical cancer, endometrial carcinoma, salivary glandcarcinoma, mesothelioma, kidney cancer, vulval cancer, thyroid cancer,hepatic carcinoma, skin cancer, melanoma, brain cancer, neuroblastoma,myeloma, various types of head and neck cancer, acute lymphoblasticleukemia, acute myeloid leukemia, Ewing sarcoma and peripheralneuroepithelioma.

As used herein, the term “chronic persistent infection” refers to thepresence of an infectious agent in the subject which is not readilycontrolled by the subject's immune system or available therapies.Examples include, but are not limited to, infections with Mycobacteriumtuberculosis (which causes tuberculosis), the Hepatitis B virus, theHepatitis C virus or retroviruses such as HIV. To be classified as a“chronic persistent infection” it is preferred that the subject has atleast had the infection for 3 months, more preferably at least 6 months.

As used herein, a “degenerative disease” is a condition that results inthe loss of cells. Preferably, the degenerative disease is aneurodegenerative disease which is marked by the loss of nerve cells.Examples of neurodegenerative diseases relevant to the present inventioninclude, not are not limited to, Alexander disease, Alzheimer disease,Amyotrophic lateral sclerosis (Lou Gehrigs' disease), AtaxiaTelangiectasia, Canavan disease, Cockayne syndrome, CorticobasalDegeneration, Huntington disease, Kennedy's disease, Krabbe disease,Lewy body dementia, Machado-Joseph disease, Parkinson disease,Pelizaeus-Merzbacher Disease, Pick's disease, Primary lateral sclerosis,Refsum's disease, Sandhoff disease, Schilder's disease,Steele-Richardson-Olszewski disease, Tabes dorsalis, and prion relateddiseases such as Creutzfeldt-Jakob disease, Alper's disease, Kuru,Gersymann-Straussler-Scheinker syndrome, Fatal familial insomnia,scrapie, transmissible milk encephalopathy, chronic wasting disease, andbovine spongiform encephalopathy. In another embodiment, thedegenerative disease is an “amyloid related disease”, examples of whichinclude, but are not limited to, Alzheimer disease, Type II diabetes andcerebral amyloid angiopathy.

As used herein the term “disease characterized by the production ofeffector T cells” refers to any condition wherein the number or activityof effector T cells plays a role in prolonging the disease state. Thesedisease are either i) typically characterized by an immune responseagainst self antigens known generally in the art as autoimmune diseases,or ii) involve a subjects immune response during organ/tissue/celltransplantation from a suitable donor. Examples of such disease include,but are not limited to, autoimmune diseases and transplant rejectionsincluding both graft-versus-host disease and host-versus-graft disease.

As used herein, the term “autoimmune disease” refers to any disease inwhich the body produces an immunogenic (ie, immune system) response tosome constituent of its own tissue. In other words the immune systemloses its ability to recognize some tissue or system within the body as“self” and targets and attacks it as if it were foreign. Autoimmunediseases can be classified into those in which predominantly one organis affected (eg, hemolytic anemia and anti-immune thyroiditis), andthose in which the autoimmune disease process is diffused through manytissues (eg, systemic lupus erythematosus). Examples of autoimmunediseases include, but are not limited to, rheumatoid arthritis, multiplesclerosis, lupus erythematosis, myasthenia gravis, scleroderma, Crohn'sdisease, ulcerative colitis, Hashimoto's disease, Graves' disease,Sjogren's syndrome, polyendocrine failure, vitiligo, peripheralneuropathy, autoimmnune polyglandular syndrome type I, acuteglomerulonephritis, Addison's disease, adult-onset idiopathichypoparathyroidism (AOIH), alopecia totalis, amyotrophic lateralsclerosis, ankylosing spondylitis, autoimmune aplastic anemia,autoimmune hemolytic anemia, Behcet's disease, Celiac disease, chronicactive hepatitis, CREST syndrome, dermatomyositis, dilatedcardiomyopathy, eosinophilia-myalgia syndrome, epidermolisis bullosaacquisita (EBA), giant cell arteritis, Goodpasture's syndrome,Guillain-Barr syndrome, hemochromatosis, Henoch-Schonlein purpura,idiopathic IgA nephropathy, insulin-dependent diabetes mellitus (IDDM),juvenile rheumatoid arthritis, Lambert-Eaton syndrome, linear IgAdermatosis, myocarditis, narcolepsy, necrotizing vasculitis, neonatallupus syndrome (NLE), nephrotic syndrome, pemphigoid, pemphigus,polymyositis, primary sclerosing cholangitis, psoriasis,rapidly-progressive glomerulonephritis (RPGN), Reiter's syndrome,stiff-man syndrome, inflammatory bowel disease, osteoarthritis andthyroiditis.

The term “transplant” and variations thereof refers to the insertion ofa graft into a host, whether the transplantation is allogeneic (wherethe donor and recipient are of different genetic origins but of the samespecies), or xenogeneic (where the donor and recipient are fromdifferent species). Thus, in a typical scenario, the host is human andthe graft is an isograft, derived from a human of the same or differentgenetic origins. In another scenario, the graft is derived from aspecies different from that into which it is transplanted, such as ababoon heart transplanted into a human recipient host, and includinganimals from phylogenically widely separated species, for example, a pigheart valve, or animal beta islet cells or neuronal cells transplantedinto a human host. Cells, tissues and/or organs may be transplanted,examples include, but are not limited to, isolated cells such as isletcells; tissue such as the amniotic membrane of a newborn, bone marrow,hematopoietic precursor cells, and ocular tissue, such as cornealtissue; and organs such as skin, heart, liver, spleen, pancreas, thyroidlobe, lung, kidney, tubular organs (e.g., intestine, blood vessels, oresophagus), etc. The tubular organs can be used to replace damagedportions of esophagus, blood vessels, or bile duct. The skin grafts canbe used not only for burns, but also as a dressing to damaged intestineor to close certain defects such as diaphragmatic hernia. The graft isderived from any mammalian source, including human, whether fromcadavers or living donors. Preferably the graft is bone marrow or anorgan such as heart.

As used herein, the term “graft-versus-host disease” refers to is animmune attack on the recipient by cells from a donor. Whilst thetransplanted cells can be of any cell type, typically the onlytransplanted tissues that house enough immune cells to cause graftversus host disease are the blood and the bone marrow.

As used herein, the term “host-versus-graft disease” refers to thelymphocyte-mediated reactions of a host against allogeneic or xenogeneiccells acquired as a graft or otherwise, which lead to damage or/anddestruction of the grafted cells. This is the common basis of graftrejection.

As used herein, “transplant rejection” or variations thereof refers tothe host's immune system mounting an immune response to the graft,ultimately resulting in the graft being rejected by the host. There aregenerally two types of “transplant rejection”, namely graft-versus-hostdisease and host-versus-graft disease.

As used herein, the term “a molecule associated with the disease” refersto any molecule which is linked to the disease state. In a preferredembodiment, the marker is a protein, or a nucleic acid encoding thereforsuch as a gene or an mRNA. Such protein and nucleic acid markers arewell known in the art. For example, levels of amyloid-β peptide can be amarker of Alzheimer's disease, and prion proteins in theirβ-confirmation can be a marker of prion related diseases. Examples ofsuitable tumour antigen markers include, but are not limited to, for AFP(marker for hepatocellular carcinoma and germ-cell tumours), CA 15-3(marker for numerous cancers including breast cancer), CA 19-9 (markerfor numerous cancers including pancreatic cancer and biliary tracttumours), CA 125 (marker for various cancers including ovarian cancer),calcitonin (marker for various tumours including thyroid medullarycarcinoma), catecholamines and metabolites (phaeochromoctoma), CEA(marker for various cancers including colorectal cancers and othergastrointestinal cancers), hCG/beta hCG (marker for various cancersincluding germ-cell tumours and choriocarcinomas), 5HIAA in urine(carcinoid syndrome), PSA (prostate cancer), sertonin (carcinoidsyndrome) and thyroglobulin (thyroid carcinoma). Suitable markers for,if not all, infectious diseases are also well known, for example the gagor env proteins of HIV.

As used herein, the term “monitoring” or variations thereof refers tothe analysis of the levels of a biomarker over a sufficient period oftime to suitably characterize the periodicity of the cycling of thebiomarker, or the timing and/or rate of the at least initial increase ordecrease in amount of the biomarker following “resetting” the immunesystem. Examples of suitable time periods and frequency of analysis aredescribed herein. Generally, the monitoring/analysis will be performedon samples obtained from the subject. However, in some instances themonitoring/analysis will be performed directly on the subject, such asthe determination of body temperature.

The “sample” refers to a material suspected of containing the biomarkersuch as regulator T cells, effectors cells, immune system markers and/ora molecule associated with the disease. The sample can be used asobtained directly from the source or following at least one step of(partial) purification. The sample can be prepared in any convenientmedium which does not interfere with the method of the invention.Typically, the sample is an aqueous solution or biological fluid asdescribed in more detail below. The sample can be derived from anysource, such as a physiological fluid, including blood, serum, plasma,saliva, sputum, ocular lens fluid, buccal swab, sweat, faeces, urine,milk, ascites fluid, mucous, synovial fluid, peritoneal fluid,transdermal exudates, pharyngeal exudates, bronchoalveolar lavage,tracheal aspirations, cerebrospinal fluid, semen, cervical mucus,vaginal or urethral secretions, amniotic fluid, and the like.Preferably, the sample is blood or a fraction thereof. Pretreatment mayinvolve, for example, preparing plasma from blood, diluting viscousfluids, and the like. Methods of treatment can involve filtration,distillation, separation, concentration, inactivation of interferingcomponents, and the addition of reagents. The selection and pretreatmentof biological samples prior to testing is well known in the art and neednot be described further. In some embodiments, due to current technologya drop of blood from a finger prick will be a sufficient sample, forexample for testing acute phase inflammatory marker levels.

The term “subject” as used herein is intended to mean any animal, inparticular mammals, such as humans, horses, cows, cats and dogs, andmay, where appropriate, be used interchangeably with the term “patient”.Preferably, the subject is a human.

The term “antibody” as used in this invention includes intact moleculesas well as molecules comprising or consisting of fragments thereof, suchas Fab, F(ab′)2, and Fv which are capable of binding an epitopicdeterminant. These antibody fragments retain some ability to selectivelybind to the target molecule such as CD8 or CD4, examples of whichinclude, but are not limited to, the following:

(1) Fab, the fragment which contains a monovalent antigen-bindingfragment of an antibody molecule can be produced by digestion of wholeantibody with the enzyme papain to yield an intact light chain and aportion of one heavy chain;

(2) Fab′, the fragment of an antibody molecule can be obtained bytreating whole antibody with pepsin, followed by reduction, to yield anintact light chain and a portion of the heavy chain; two Fab′ fragmentsare obtained per antibody molecule;

(3) (Fab′)₂, the fragment of the antibody that can be obtained bytreating whole antibody with the enzyme pepsin without subsequentreduction; F(ab)₂ is a dimer of two Fab′ fragments held together by twodisulfide bonds;

(4) Fv, defined as a genetically engineered fragment containing thevariable region of the light chain and the variable region of the heavychain expressed as two chains;

(5) Single chain antibody (“SCA”), defined as a genetically engineeredmolecule containing the variable region of the light chain, the variableregion of the heavy chain, linked by a suitable polypeptide linker as agenetically fused single chain molecule; such single chain antibodiesmay be in the form of multimers such as diabodies, triabodies, andtetrabodies etc which may or may not be polyspecific (see, for example,WO 94/07921 and WO 98/44001) and

(6) Single domain antibody, typically a variable heavy domain devoid ofa light chain.

Computer Modelling of Biomarkers

FIG. 1 illustrates a distributed system 100 for obtaining cyclingbiomarker measurements, centrally processing such measurements, anddetermining a suitable future time for administration of a therapy. Ameasurement device 110 measures biomarker levels of a plurality ofdiseased subjects in multiple locations. Obtained measurements arecommunicated via a wide area communications network such as the Internet120 to a central computing device 130. For each individual, thecomputing device 130 estimates from the measurements a future time atwhich a therapy should be administered to, for example, increase thechance of progression free survival. The central computing device 130may be a server and the measurement device 110 may be a desktopcomputer, a laptop computer wireless device such as a smartphone, or adedicated computing device.

In a first application, the measurement device 110 is operable to obtainmeasurements of a biomarker which cycles in time in a subject; see step210 in FIG. 2. The measurements are then sent to the central computingdevice 130 via the Internet 120; see step 220. Alternatively oradditionally, the measurements may be sent to a remote data store forretrieval by the central computing device.

At the central computing device 130, the measurements are received orretrieved from the data store, and analysed to estimate a periodicity ofthe cycling of the biomarker; see steps 310 and 320 in FIG. 3. From theestimated periodicity of the cycling of the biomarker, the centralcomputing device 130 then determines a preferred time to administer thetherapy and sends the estimated periodicity and/or the preferred time tothe measurement device 110 via the Internet 120; see steps 330 and 340in FIG. 3.

At the measurement device 110, the estimated periodicity and/or thepreferred time of administration is determined; see step 230 in FIG. 2.Alternatively or additionally, the estimated periodicity, or thepreferred time, or both, is sent to another computing device that isindependent of the measurement device 110.

Instead of relying on the analysis of the cycling of the immune systemto treat the disease, the immune system can at least be partially“reset” and the emerging T cell population of interest (effector orregulator depending on the disease) can be targeted at the appropriatetime in a second application. Referring now to FIG. 4 and FIG. 5, themeasurement device 110 is operable to obtain measurements of a biomarkerin a subject where the biomarker at least initially increases ordecreases in amount following being treated for the disease; see step410 in FIG. 4. In this case, the measurements were at least obtainedafter said treatment. The measurements are then sent to the centralcomputing device 130 via the Internet 120; see step 420. Alternativelyor additionally, the measurements may be sent to a remote data store forretrieval by the central computing device.

At the central computing device 130, the measurements are received orretrieved from the data store; see step 510 in FIG. 5. The centralcomputing device 130 then analyses the measurements to estimate thetiming and/or rate of the at least initial increase or decrease inamount of the biomarker; see step 520 in FIG. 5. From the estimatedtiming and rate, the central computing device 130 then determines apreferred time in the future to administer the therapy; see step 530.The computing device 130 then sends the estimated timing and/or rate tothe measurement device 110 via the Internet 120; see step 540.Alternatively or additionally, the preferred time is sent to themeasurement device.

At the measurement device 110, the estimated timing and/or rate, and/orthe preferred time of administration is determined; see step 430 in FIG.4. Alternatively or additionally, the estimated timing and/or rate andpreferred time is be sent to another computing device that isindependent of the measurement device 110.

Some portions of this detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

As such, it will be understood that such acts and operations, which areat times referred to as being computer-executed, include themanipulation by the processing unit of the computer of electricalsignals representing data in a structured form. This manipulationtransforms the data or maintains it at locations in the memory system ofthe computer, which reconfigures or otherwise alters the operation ofthe computer in a manner well understood by those skilled in the art.The data structures where data is maintained are physical locations ofthe memory that have particular properties defined by the format of thedata. However, while the invention is described in the foregoingcontext, it is not meant to be limiting as those of skill in the artwill appreciate that various of the acts and operations described mayalso be implemented in hardware.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the description, it isappreciated that throughout the description, discussions utilizing termssuch as “processing” or “computing” or “calculating” or “determining” or“displaying” or “obtaining” or “projecting” or “analysing” or “imposing”or the like, refer to the action and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.

The present invention also relates to apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, and each coupledto a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description. Inaddition, the present invention is not described with reference to anyparticular programming language. It will be appreciated that a varietyof programming languages may be used to implement the teachings of theinvention as described herein.

A machine-readable medium includes any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer). For example, a machine-readable medium includes read onlymemory (“ROM”); random access memory (“RAM”); magnetic disk storagemedia; optical storage media; flash memory devices; electrical, optical,acoustical or other form of propagated signals (e.g., carrier waves,infrared signals, digital signals, etc.); etc.

The invention is illustrated as being implemented in a suitablecomputing environment (FIG. 6). Although not required, the inventionwill be described in the general context of computer-executableinstructions, such as program modules, being executed by a personalcomputer. Generally, program modules include routines, programs,objects, components, data structures, etc. that perform particular tasksor implement particular abstract data types. Moreover, those skilled inthe art will appreciate that the invention may be practiced with othercomputer system configurations, including hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. The invention may be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

In FIG. 6, a general purpose computing device is shown in the form of aconventional personal computer 20, including a processing unit 21, asystem memory 22, and a system bus 23 that couples various systemcomponents including the system memory to the processing unit 21. Thesystem bus 23 may be any of several types of bus structures including amemory bus or memory controller, a peripheral bus, and a local bus usingany of a variety of bus architectures. The system memory includes readonly memory (ROM) 24 and random access memory (RAM) 25. A basicinput/output system (BIOS) 26, containing the basic routines that helpto transfer information between elements within the personal computer20, such as during start-up, is stored in ROM 24. The personal computer20 further includes a hard disk drive 27 for reading from and writing toa hard disk 60, a magnetic disk drive 28 for reading from or writing toa removable magnetic disk 29, and an optical disk drive 30 for readingfrom or writing to a removable optical disk 31 such as a CD ROM or otheroptical media.

The hard disk drive 27, magnetic disk drive 28, and optical disk drive30 are connected to the system bus 23 by a hard disk drive interface 32,a magnetic disk drive interface 33, and an optical disk drive interface34, respectively. The drives and their associated computer-readablemedia provide nonvolatile storage of computer readable instructions,data structures, program modules and other data for the personalcomputer 20. Although the exemplary environment shown employs a harddisk 60, a removable magnetic disk 29, and a removable optical disk 31,it will be appreciated by those skilled in the art that other types ofcomputer readable media which can store data that is accessible by acomputer, such as magnetic cassettes, flash memory cards, digital videodisks, Bernoulli cartridges, random access memories, read only memories,storage area networks, and the like may also be used in the exemplaryoperating environment.

A number of program modules may be stored on the hard disk 60, magneticdisk 29, optical disk 31, ROM 24 or RAM 25, including an operatingsystem 35, one or more applications programs 36, other program modules37, and program data 38. A user may enter commands and information intothe personal computer 20 through input devices such as a keyboard 40 anda pointing device 42. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit21 through a serial port interface 46 that is coupled to the system bus,but may be connected by other interfaces, such as a parallel port, gameport or a universal serial bus (USB) or a network interface card. Amonitor 47 or other type of display device is also connected to thesystem bus 23 via an interface, such as a video adapter 48. In additionto the monitor, personal computers typically include other peripheraloutput devices, not shown, such as speakers and printers.

The personal computer 20 may operate in a networked environment usinglogical connections to one or more remote computers, such as a remotecomputer 49. The remote computer 49 may be another personal computer, aserver, a router, a network PC, a peer device or other common networknode, and typically includes many or all of the elements described aboverelative to the personal computer 20, although only a memory storagedevice 50 has been illustrated. The logical connections depicted includea local area network (LAN) 51 and a wide area network (WAN) 52. Suchnetworking environments are commonplace in offices, enterprise-widecomputer networks, intranets and, inter alia, the Internet.

When used in a LAN networking environment, the personal computer 20 isconnected to the local network 51 through a network interface or adapter53. When used in a WAN networking environment, the personal computer 20typically includes a modem 54 or other means for establishingcommunications over the WAN 52. The modem 54, which may be internal orexternal, is connected to the system bus 23 via the serial portinterface 46. In a networked environment, program modules depictedrelative to the personal computer 20, or portions thereof, may be storedin the remote memory storage device. It will be appreciated that thenetwork connections shown are exemplary and other means of establishinga communications link between the computers may be used.

Therapy

The present invention relates broadly to the use of three differenttypes of therapies. There are:

1) therapies which are specific for effector T cells (such as CD8+specific antibodies) that can be used to treat a disease characterizedby the production of effector T cells,

2) therapies which are specific for regulator T cells (such as CD4+specific antibodies) that can be used to treat a disease characterizedby the production of regulator T cells, and

3) non-selective therapies which influence effector T cells andregulator T cells, however, the timing of administration of the therapydictates the cell type being targeted.

The current analysis indicates that there is about a 12 hour time periodto administer the therapy in each cycle, and/or soon after “re-setting”the immune system. In a preferred embodiment, the therapy is oral, hashigh bioavailability, has low toxicity to the patient and/or has a halflife of 6 to 15 hours. Examples of such therapies include alkalatingagents, vinca alkaloids and taxanes.

Therapies for Treating a Disease Characterized by the Production ofRegulator T Cells

The therapy can be any agent, factor or treatment which selectively ornon-selectively results in the destruction, limits the function of, orthe inhibition of the production, of regulator T cells. For example, aCD4+ specific antibody could be used to specifically target CD4+ Tcells. However, in some instances a non-selective therapy could be used,such as an anti-proliferative drug, an anti-metabolic drug or radiation,each of which target dividing cells. In particular, as with other celltypes, regulator T cells are particularly vulnerable to destruction byanti-mitotic (anti-proliferative) drugs or spindle poisons (e.g.vinblastine or paclitaxel) when dividing and specifically in mitosis.

Preferably, the therapy is administered, or the estimation of thepreferred time to administer, is such that the activity of the effectorT cells is not significantly reduced. More specifically, the timing issuch that the therapy exerts a proportionally greater effect against theregulator T cells than the effector T cells. It is clearly preferredthat the agent is administered at a time when the ratio of effectagainst the regulator T cells to the effect against effector T cells isgreatest. In a preferred embodiment, the therapy for treating a diseasecharacterized by the production of regulator T cells is administeredjust before or just after regulator T cells begin clonally expanding.

The term “anti-proliferative drug” and “anti-metabolic drug” is a termwell understood in the art and refers to any compound that destroysdividing cells or inhibits them from undergoing further proliferation.Anti-proliferative drugs include, but are not limited to,mechlorethamine, temozolomide, cyclophosphamide, ifosfamide, melphalan,chlorambucil, hexamethyl-melamine, thiotepa, busulfan, carmustine,lomustine, semustine, streptozocin, dacarbazine, methotrexate,fluorouracil, floxuridine, cytarabine, mercaptopurine, thioguanine,pentostatin, vinblastine, anhydro vinblastine, vincristine, etoposide,teniposide, dactinomycin, daunorubicin, doxorubicin, bleomycin,plicamycin, mitomycin, L-asparaginase, cisplatin, mitoxantrone,hydroxyurea, procarbazine, mitotane, aminoglutethimide, prednisone,hydroxyprogesterone caproate, medroprogesterone acetate, megestrolacetate, diethylstilbestrol, ethinyl estradiol, tamoxifen, testosteronepropionate, radioactive isotopes, ricin A chain, taxol, diphtheriatoxin, colchicine and pseudomonas exotoxin A.

Recent studies have suggested that CD4+CD25+ T cells play an importantrole in regulating immune cells directed against self antigens (Salomonet al, 2000; Suri-Payer and Cantor, 2001). Furthermore, targetingCD4+CD25+ T cells has been shown to enhance the ability of an animal tocontrol tumour growth (Onizuka et al., 1999; Shimizu et al., 1999;Sutmuller et al., 2001). Accordingly, CD4+CD25+ T cells could be actingas regulator T cells as used herein. The activity of CD4+CD25+ T cellscan be downregulated by anti-GITR, anti-CD28 and/or anti-CTLA-4 (Read etal., 2000; Takahashi et al., 2000; Shimizu et al., 2002). Thus, theseantibodies may be useful as agents for use in the methods of the presentinvention.

The therapy is usually administered in the dosage forms that are readilyavailable to the skilled clinician, and are generally administered intheir normally prescribed amounts (as for example, the amounts describedin the Physician's Desk Reference, 55th Edition, 2001, or the amountsdescribed in the manufacture's literature for the use of the therapy).

In one embodiment, the therapy is administered as a single bolusinjection. In another embodiment, the therapy is administered byinfusion. The period of infusion can be, for example, at least 3 hours,at least 12 hours or at least 24 hours.

It has also determined that treatment for a disease characterized by theproduction of regulator T cells can be enhanced (or the chances ofsuccessful treatment can be increased) when the vaccine is administeredat the appropriate time. In these instances, the vaccine boosts theinnate immune response against the disease. This will most likely be aresult of increased numbers and/or activity of effector T cells.Although theoretically regulator T cells will still ultimately beproduced, the boosting of the immune system allows the subject tosuitably control the disease before the emergence of the regulator Tcells. This scenario would explain why previous studies have shown thatanti-HIV and anti-tumour vaccines are only successful in a small numberof subjects. More specifically, there is only a small chance the vaccinewill be administered at the same time the innate immune response to thedisease is occurring. Other times of administration in the prior artoccur when there are high numbers and/or activity of regulators cells,or at times which uncouple the natural cycling of the immune system.

Such a vaccine will comprise at least one antigen, or a polynucleotideencoding said antigen. The vaccine can be provided as any form known inthe art such as, but not limited to, a DNA vaccine, ingestion of atransgenic organism expressing the antigen, or composition comprisingthe antigen.

As used herein, an “antigen” is any polypeptide sequence that containsan epitope which is capable of producing an immune response against thedisease.

Antigens which are capable of raising an immune response against acancer cell are well known in the art. Certain tumour antigens can berecognized and targeted by the immune system. This property may be dueto overexpression by the tumour tissue. Some of these antigens can bedetected in normal tissue. The tumour antigens targeted by T cells aregenerally proteins that are processed intracellularly and presented asshort peptide fragments bound in the groove of the tumour MHC class 1molecule to be recognized by CD8⁺ cytotoxic T lymphocytes. The merepresence of a tumour antigen is not always sufficient to trigger animmune response. Co-stimulatory molecules such as B7.1 are sometimesrequired. Once antigen-specific T cells are stimulated, they are capableof recognizing and destroying the tumour. The conditions needed for theactivation of antigen-specific T cells are stringent, but are open togenetic manipulation of target tumour cells and T cells.

Antigens which can be used to treat infections, such as HIV, are alsowell known in the art.

The antigen can be provided in any manner known in the art which leadsto an immune response. An antigen can be, for example, native,recombinant or synthetic. Native antigens can be prepared, for example,by providing cell lysates of a tumour cell.

Vaccines may be prepared from one or more antigens. The preparation ofvaccines which contain an antigen is known to one skilled in the art.Typically, such vaccines are prepared as injectables, or orals, eitheras liquid solutions or suspensions; solid forms suitable for solutionin, or suspension in, liquid prior to injection or oral consumption mayalso be prepared. The preparation may also be emulsified, or the proteinencapsulated in liposomes. The antigen is often mixed withcarriers/excipients which are pharmaceutically acceptable and compatiblewith the active ingredient. Suitable carriers/excipients are, forexample, water, saline, dextrose, glycerol, ethanol, or the like andcombinations thereof.

In addition, if desired, the vaccine may contain minor amounts ofauxiliary substances such as wetting or emulsifying agents, pH bufferingagents, and/or adjuvants which enhance the effectiveness of the vaccine.

Typically, vaccines comprise an adjuvant. As used herein, the term“adjuvant” means a substance that non-specifically enhances the immuneresponse to an antigen. Examples of adjuvants which may be effectiveinclude but are not limited to:N-acetyl-muramyl-L-threonyl-D-isoglutamine (thr-MDP),N-acetyl-nor-muramyl-L-alanyl-D-isoglutamine (CGP 11637, referred to asnor-MDP),N-acetylmuramyl-L-alanyl-D-isoglutaminyl-L-alanine-2-(1′-2′-dipalmitoyl-sn-glycero-3-hydroxyphosphoryloxy)-ethylamine(CGP 19835A, referred to as MTP-PE), and RIBI, which contains threecomponents extracted from bacteria, monophosphoryl lipid A, trehalosedimycolate and cell wall skeleton (MPL+TDM+CWS) in a 2% squalene/Tween80 emulsion. Further examples of adjuvants include aluminum hydroxide,aluminum phosphate, aluminum potassium sulfate (alum), bacterialendotoxin, lipid X, Corynebacterium parvum (Propionobacterium acnes),Bordetella pertussis, polyribonucleotides, sodium alginate, lanolin,lysolecithin, vitamin A, saponin, liposomes, levamisole, DEAE-dextran,blocked copolymers or other synthetic adjuvants. Such adjuvants areavailable commercially from various sources, for example, Merck Adjuvant65 (Merck and Company, Inc., Rahway, N.J.) or Freund's IncompleteAdjuvant and Complete Adjuvant (Difco Laboratories, Detroit, Mich.).

The proportion of antigen and adjuvant can be varied over a broad rangeso long as both are present in effective amounts. For example, aluminiumhydroxide can be present in an amount of about 0.5% of the vaccinemixture (Al₂O₃ basis). Conveniently, the vaccines are formulated tocontain a final concentration of antigenic polypeptide in the range offrom 0.2 to 200 μg/ml, preferably 5 to 50 μg/ml, most preferably 15μg/ml.

After formulation, the vaccine may be incorporated into a sterilecontainer which is then sealed and stored at a low temperature, forexample 4° C., or it may be freeze-dried. Lyophilisation permitslong-term storage in a stabilised form.

The vaccines are conventionally administered parenterally, by injection,for example, either subcutaneously or intramuscularly. Additionalformulations which are suitable for other modes of administrationinclude suppositories and, in some cases, oral formulations. Forsuppositories, traditional binders and carriers may include, forexample, polyalkylene glycols or triglycerides; such suppositories maybe formed from mixtures containing the active ingredient in the range of0.5% to 10%, preferably 1% to 2%. Oral formulations include suchnormally employed excipients as, for example, pharmaceutical grades ofmannitol, lactose, starch, magnesium stearate, sodium saccharine,cellulose, magnesium carbonate, and the like. These compositions takethe form of solutions, suspensions, tablets, pills, capsules, sustainedrelease formulations or powders and contain 10% to 95% of activeingredient, preferably 25% to 70%. Where the vaccine composition islyophilised, the lyophilised material may be reconstituted prior toadministration, e.g. as a suspension. Reconstitution is preferablyeffected in buffer.

Capsules, tablets and pills for oral administration to a patient may beprovided with an enteric coating comprising, for example, Eudragit “S”,Eudragit “L”, cellulose acetate, cellulose acetate phthalate orhydroxypropylmethyl cellulose.

DNA vaccination involves the direct in vivo introduction of DNA encodingan antigen into tissues of a subject for expression of the antigen bythe cells of the subject's tissue. Such vaccines are termed herein “DNAvaccines” or “nucleic acid-based vaccines”. DNA vaccines are describedin U.S. Pat. No. 5,939,400, U.S. Pat. No. 6,110,898, WO 95/20660 and WO93/19183.

To date, most DNA vaccines in mammalian systems have relied upon viralpromoters derived from cytomegalovirus (CMV). These have had goodefficiency in both muscle and skin inoculation in a number of mammalianspecies. A factor known to affect the immune response elicited by DNAimmunization is the method of DNA delivery, for example, parenteralroutes can yield low rates of gene transfer and produce considerablevariability of gene expression. High-velocity inoculation of plasmids,using a gene-gun, enhanced the immune responses of mice, presumablybecause of a greater efficiency of DNA transfection and more effectiveantigen presentation by dendritic cells. Vectors containing the nucleicacid-based vaccine of the invention may also be introduced into thedesired host by other methods known in the art, e.g., transfection,electroporation, microinjection, transduction, cell fusion, DEAEdextran, calcium phosphate precipitation, lipofection (lysosome fusion),or a DNA vector transporter.

Transgenic plants producing a antigenic polypeptide can be constructedusing procedures well known in the art. A number of plant-derived ediblevaccines are currently being developed for both animal and humanpathogens. Immune responses have also resulted from oral immunizationwith transgenic plants producing virus-like particles (VLPs), orchimeric plant viruses displaying antigenic epitopes. It has beensuggested that the particulate form of these VLPs or chimeric virusesmay result in greater stability of the antigen in the stomach,effectively increasing the amount of antigen available for uptake in thegut.

Another example of an therapy which can be administered in a method ofthe invention is dsRNA. dsRNA is used in RNA interference (RNAi) whichis a phenomenon where upon introduction into a cell, mRNA homologous tothe dsRNA is specifically degraded so that synthesis of gene products issuppressed. Examples of such an agent causing RNAi include, but are notlimited to, a sequence having at least about 70% homology to the nucleicacid sequence of a target gene or a sequence hybridizable understringent conditions, RNA containing a double-stranded portion having alength of at least 10 nucleotides or variants thereof. Examples oftarget genes include, but are not limited to, a gene required forreplication or survival of a regulator T cell.

dsRNA having a length of about 20 bases (e.g., representatively about 21to 23 bases) or less than about 20 bases, which is called siRNA in theart, can be used. Expression of siRNA in cells can suppress expressionof a gene targeted by the siRNA. In another embodiment, an agent capableof causing RNAi may have a short hairpin structure having a stickyportion at the 3′ terminus (shRNA; short hairpin RNA). As used herein,the term “shRNA” refers to a molecule of about 20 or more base pairs inwhich a single-stranded RNA partially contains a palindromic basesequence and forms a double-strand structure therein (i.e., a hairpinstructure). shRNA can be artificially chemically synthesized.Alternatively, shRNA can be produced by linking sense and antisensestrands of a DNA sequence in reverse directions and synthesizing RNA invitro with T7 RNA polymerase using the DNA as a template. The length ofthe double-stranded portion is not particularly limited, but ispreferably about 10 or more nucleotides, and more preferably about 20 ormore nucleotides. The 3′ protruding end may be preferably DNA, morepreferably DNA of at least 2 nucleotides in length, and even morepreferably DNA of 2-4 nucleotides in length.

An agent capable of causing RNAi useful for the invention may beartificially synthesized (chemically or biochemically) or naturallyoccurring. There is substantially no difference therebetween in terms ofthe effect of the present invention. A chemically synthesized agent ispreferably purified by liquid chromatography or the like.

An agent capable of causing RNAi used in the present invention can alsobe produced in vitro. In this synthesis system, T7 RNA polymerase and T7promoter can be used to synthesize antisense and sense RNAs fromtemplate DNA. These RNAs are annealed and thereafter are introduced intoa cell.

dsRNA can be delivered to the subject using any means known in the art.Examples of methods of delivering dsRNA to a patient are described in,for example, US 20040180357, US 20040203024 and US 20040192629.

Therapies for Treating a Disease Characterized by the Production ofEffector T Cells

The therapy can be any agent, factor or treatment which selectively ornon-selectively results in the destruction, limits the function of, orthe inhibition of the production, of effector T cells. For example, aCD8+ specific antibody could be used to specifically target CD8+ Tcells. However, in some instances a non-selective therapy could be used,such as an anti-proliferative drug, an anti-metabolic drug or radiation,each of which target dividing cells. In particular, as with other celltypes, effector T cells are particularly vulnerable to destruction byanti-mitotic (anti-proliferative) drugs or spindle poisons (e.g.vinblastine or paclitaxel) when dividing and specifically in mitosis.

Each of the above mentioned types of therapies are also useful fortreating diseases characterized by the production of effector T cells.With regard to dsRNA, the dsRNA molecule can be specific for mRNAsexpressed only in effector T cells. Furthermore, antibodies useful fortreating these diseases bind molecules present in effector T cells suchas CD8.

Preferably, for these diseases the therapy is administered, or theestimation of the preferred time to administer, is such that theactivity of the regulator T cells is not significantly reduced. Morespecifically, the timing is such that the therapy exerts aproportionally greater effect against the effector T cells than theregulator T cells. It is clearly preferred that the agent isadministered at a time when the ratio of effect against the effector Tcells to the effect against regulator T cells is greatest. In apreferred embodiment, the therapy for treating a disease characterizedby the production of effector T cells is administered just before orjust after effector T cells begin clonally expanding.

Acute Phase Inflammatory Markers

Some acute phase inflammatory markers initially increase during animmune response (referred to hereinafter as positive acute phaseinflammatory markers) whilst others initially decrease during an immuneresponse (referred to hereinafter as negative acute phase inflammatorymarkers). Acute phase inflammatory markers are also referred to in theart as acute phase reactants or acute phase proteins. The skilledaddressee will be aware of the many assays which can be used to monitoracute phase inflammatory markers.

Examples of positive acute phase inflammatory markers include, but arenot limited to, c-reactive protein, serum amyloid A, serum amyloid Pcomponent, complement proteins such as C2, C3, C4, C5, C9, B, C1inhibitor and C4 binding protein, fibrinogen, von Willebrand factor,α1-antitrypsin, α1-antichymotrypsin, α2-antiplasmin, heparin cofactorII, plasminogen activator inhibitor I, haptoglobin, haemopexin,ceruloplasmin, manganese superoxide dismutase, α1-acid glycoprotein,haeme oxygenase, mannose-binding protein, leukocyte protein I,lipoporotein (a), lipopolysaccharide-binding protein, and interleukinssuch as IL-1, IL-2, IL-6, IL-10 and receptors thereof.

Example of negative acute phase inflammatory markers include, but arenot limited to, albumin, pre-albumin, transferin, apoAI, apoAII, α2 HSglycoprotein, inter-α-trypsin inhibitor, histidine-rich glycoprotein.

Serum amyloid A (SAA) was discovered as a plasma component that sharesantigenicity with amyloid AA, the chief fibrillar component in reactiveAA amyloid deposits. SAA has been shown to be an acute phase reactantwhose level in blood is elevated to 1000-fold or higher as part of thebody's responses to various injuries including trauma, infection andinflammation.

SAA levels can be determined as known in the art, see for exampleWeinstein et al. (1984), Liuzzo et al. (1994), O'Hara et al. (2000),Kimura et al. (2001) and O'Hanlon et al. (2002).

C-reactive protein (CRP) is an important positive acute phase responseprotein, and its concentration in serum may increase as much as1,000-fold during the acute phase response. CRP is a pentamer consistingof five identical subunits, each having a molecular weight of about23,500.

C-reactive protein levels can be determined using techniques known inthe art, these include, but are not limited to, those disclosed in Senjuet al. (1983), Weinstein et al. (1984), Price et al. (1987), Liuzzo etal. (1994), Eda et al. (1998), Kimura et al. (2001) and O'Hanlon et al.(2002).

The complement proteins are a group of at least 20 immunologicallydistinct components. They normally circulate in the blood in an inactiveform. They are able to interact sequentially with antigen—antibodycomplexes, with each other and with cell membranes in a complex butadaptable way to destroy viruses and bacteria and pathologically, eventhe hosts own cells. Abnormal serum levels of complement proteins may bedue to either inherited or acquired diseases. At least circulatinglevels of C3 and C4 reflect a balance between complement consumption dueto immune complex formation and increased synthesis due to acute phaseresponse. Methods of measuring complement protein levels are well knownin the art.

Levels of different interleukins can also be determined using proceduresknown in the art such as using the ProteoPlex™ cytokine assay kit (EMDBiosciences Inc., CA, USA).

Monitoring of Subjects

In most instances, the time point that the therapy is to be administeredwill need to be empirically determined in subjects at different stagesof disease as their immune response kinetics may vary. Other factorssuch as the general health of the subject and/or the genetic makeup ofthe subject will also impact upon when is the appropriate time toadminister the therapy.

Techniques known in the art can be used to monitor the growingpopulation of effector and/or regulator T cells during the “cycle”.Serial blood samples can be collected and quantitatively screened for Tcell subsets (such as CD4+ and/or CD8+) by FACS analysis, or for acutephase marker levels as described above.

Optimally, the monitoring is continued to determine the effect of thetherapy. Insufficient ablation, re-emergence of the effector T cells orregulator T cells (depending on the disease state being treated) willmean that the method of the present invention should be repeated. Suchrepeated cycles of treatment may generate immunological memory. It istherefore possible that the present invention, used in repetitive mode,may provide some prophylactic protective effect.

Monitoring can be performed at a central testing laboratory, or at leastin some instances at some other location that is convenient for thepatient such as using a point of care device. Examples of suitable pointof care monitoring devices are produced by Universal Biosensors(Rowville, Australia) (see US 20060134713), Axis-Shield PoC AS (Oslo,Norway) and Clinical Lab Products (Los Angeles, USA).

EXAMPLES Example 1 Clinical Trial and Analysis of Data

Methods and Methods

Patients, Treatment and Monitoring

A pilot clinical study was conducted on 12 patients with metastaticmelanoma (median age 61; 4 female; 7 with M1c disease) at The MayoClinic, Rochester, Minn., USA headed by Dr Svetomir Markovic. Serial CRPmeasurements were taken every 2-3 days for 2 weeks. The CRP oscillationcycle was identified by analysis of the raw data without any computeraided modelling, and chemotherapy with temozolomide (200 mg/m2 for 5days, every 28 days) was initiated. Patients were evaluated for clinicaland immune response endpoints every 8 weeks until progression.

Analysis of Immune System Cycling

In the described embodiment, the model form is:

${\log\left( {CRP}_{i} \right)} = {{{\cos\left( {2\pi \times \left( \frac{{day}_{i} - {offset}}{period} \right)} \right)} \times {amplitude}} + {mean} + \varepsilon_{i}}$That is, the natural logarithm CRP of a patient on day i is considered aharmonic function where the parameters (period, offset, mean, andamplitude) of the curve are unknown, and are estimated from the data.The assumptions that are necessary for having reasonable faith in themodel are that: the model form is correct; the residuals ε_(i)˜N (0,σ²); and the residuals are independent.

This embodiment uses the natural logarithm because extensive testingsuggests that otherwise, the fitting algorithm is strongly affected byobservations that are unusually high. Furthermore as the CRP measurementrefers to a concentration, which is constrained to be greater than zero,the log transformation is a natural one to try.

The model form is non-linear. The consequence of the non-linearity isthat in order to estimate the model parameters, it is necessary tonominate a starting point for each one. A consequence of this necessityis that the predicted values may depend on the initial values,especially in cases when data are sparse. In order to provide someobjectivity, the present embodiment uses three start points for theperiod, for three separate fits of the model. This allows any disparitybetween the outcomes to be considered an indication of poor quality ofthe data.

Although this embodiment uses a model form defined by a sine curve toestimate the periodicity of the cycling of the biomarker, it will beappreciated that any other suitable periodic regression techniques maybe used. One example is Fourier analysis, which is suitable forapplications where the measurements do not follow a symmetrical relationas a sine curve. In this case, the measurements can be fitted into afinite Fourier series, which is a sum of finite sine and cosine curvesand allows higher harmonics to be considered.

Additionally or alternatively, machine learning algorithms may be usedto estimate the periodicity of the cycling of the biomarker, such as:

(a) Bayesian regression analysis, which involves determining a functionfor the relationship between the measurements (marker) and theperiodicity (period), and calculating a conditional posteriorprobability distribution the periodicity conditional on themeasurements, i.e. p(period| marker)

(b) Artificial neural network, where the measurements (marker) aredefined as input nodes, and the periodicity of the cycling of themeasurements (period) as output node. Each input node is multiplied by arandom weight, and the relationship between the input nodes and theoutput node is a hidden at the hidden node. The weights are estimateduntil a best-fit curve of the periodicity is obtained as a function ofthe measurements. The operation of estimating the weights is known astraining.

(c) Classification algorithms, where measurements (marker) areclassified or placed into groups based on one or more inherentcharacteristics of the measurements. Classification is one form ofsupervised machine learning, where classifiers such as neural network,support vector machines and k-means clustering can be used. Randomforest regression techniques can be used, where a random forest is acollection of tree predictors that are each built independently from theothers using a random vector.

Fit

The model fit approach proceeds as follows. The following steps arerepeated for three different starting estimates of period: 5 days, 7days, and 9 days.

1) Use an optimization algorithm from Byrd et al. (1995) that allows boxconstraints on the parameter estimates to maximise the likelihood of theobservations conditional on the model, the data, and the assumedunderlying distribution. This is done for t with degrees of freedomamong 3, 5, 10, 20, 40, 70, 100, and 1000, which differ in theirtolerance to outliers. The box constraints help guide the optimizationinto biologically realistic regions.

2) If the model fits described in the previous step fail, the strategyis repeated using the same algorithm but without the box constraints.

3) The model that has the highest log-likelihood is chosen.

4) The parameter estimates are used to estimate the remaining time untilthe next peak from the assumed current day, which depends on the delaysince the final measurement.

5) The parameter estimates, their asymptotic standard errors, and theestimated wait time are reported. These factors are integrated by theanalyst to establish the best possible time for treatment, or the bestpossible time for further measurements, conditional on the model.

Assessment

An assessment of the quality of the fit and the relative reliability ofthe estimates, including the estimated time to wait until treatment, isprovided by the construction of confidence intervals. The confidenceintervals are based on the bootstrap technology (Davison and Hinkley,1997), using the so-called parametric, normal bootstrap intervals.

These randomly-generated intervals are designed to cover the true valueabout 95% of the time, but their behaviour is guaranteed only in verylarge sample sizes. Normal intervals appear to have the best overallpattern of behaviour.

Interpretation

Each patient report comprises two panels; (1) a plot of patient data,and (2) a plot of patient data with models overlaid. We omit the firstpanel to save space. Each plot shows a vertical grey line; this linerepresents ‘today’ and allows for the fact that there will most often bea delay between the taking of the final measurement and the time of dataanalysis. For example, in FIG. 7, the analysis took place on August 14,and the last measurement was taken on August 9.

The second panel provides a prediction of when to treat the patient(currently aiming for the peak of the cycle), along with a considerableamount of diagnostic information. The estimated optimal wait time (indays) is the presented in the first column of the legend box.

The diagnostic information is used to assess the quality of theprediction.

-   -   the panel contains up to three model lines. The overlaying of        the lines introduces some visual confusion. This is an asset, as        it reflects the confusion of the algorithm about the true model.        -   These three lines represent three different starting points            for the model. Ideally the three will be coincident, which            implies that the model predictions are identical regardless            of the starting point. If the three lines are not            coincident, then more measurements are required, and should            be taken at times when the projected lines are as far apart            as possible, for best resolution.        -   If more data cannot be obtained then the line that            corresponds to the highest value of 11 (log-likelihood,            reported in box) should be used, subject to the following            qualifiers.        -   The legend box also reports the estimated period, P.            Previous examinations of these data suggest that a period of            close to 7 is common. Periods far from 7 should be treated            with suspicion.        -   A visual examination and comparison of the curves should be            undertaken. It is sometimes possible to distinguish between            competing curves by eye if the statistics are ambiguous.    -   When the curve is chosen, it must be interpreted. The legend box        reports WL, which is the approximate length of the 95%        confidence interval of the expected wait time, in days. This        quantity reports the data-based uncertainty of the wait time. If        this number is too high, then refer to WO. WO is the approximate        length of the 95% confidence interval of the offset. If this        number is low when WL is high, then the high variability in the        wait time is an artifact of how close ‘today’ is to the best        treatment time. In that case, we will worry less about the        variability of WL. If both WL and OL are high, then more data        are needed.        Results

The results of each of the patients is described below which includes arating system used to try to identify those patients whose data could bereliably used by the model. The rating was from 1 (worst) to 5 (best).Rating 5 indicated that the inventors were satisfied that the datamatched the model as well as could be expected. Rating 1 indicated thatthe inventors had little expectation that the model would be reliable.

Patient 1

The treatment appears to have been applied just before the peak ofimmune system activity (FIG. 7). WL is very large but OL is muchsmaller; this suggests that the size of WL is an artifact of the modeldefinition. However, there is more than one curve, so the rating waspenalized—Rating: 4.

Patient 2

Analysis of the models suggests the drug was administered to patient 2when the immune system peaked (FIG. 8). WL is very high but OL very low,this suggests that the size of WL is an artifact of the modeldefinition. There is more than one curve, so the rating waspenalized—Rating: 4.

Patient 3

Analysis of the models suggests the drug was administered to patient 3on the down-swing of the immune system (FIG. 9). WL is low for all threecurves. There is more than one curve, so the rating waspenalized—Rating: 4.

Patient 4

The analysis suggests the treatment was just past halfway up the slope(FIG. 10), however, more sampling would have provided a clearerdetermination—Rating: 1.

Patient 5

The treatment appears to have been applied just after the peak of immunesystem activity (FIG. 11). WL is low. There is more than one curve, sothe rating was penalized—Rating: 4.

Patient 6

Unfortunately, the treatment was applied on the day of the thirdmeasurement, thus more data would have been preferable—Rating: 1 (FIG.12). The available data suggests the treatment was applied on thebeginning of the downswing of the immune cycle.

Patient 7

Analysis suggests treatment was applied some time after the immunesystem peak (FIG. 13), however, more sampling would have provided aclearer determination—Rating: 1.

Patient 8

For patient 8 the curves agree on the relative location of thetreatment: at the peak of immune system activity (FIG. 14). There ismore than one curve, so the rating was penalized—Rating: 4.

Patient 9

The relative location of the treatment is on the downswing of immunesystem activity (FIG. 15). The inventors have reasonable confidence onthe location for the data for this patient—Rating: 4.

Patient 10

Fortunately the curves basically agree on the location of the treatment:just on or after the peak (FIG. 16). WL is high—Rating: 3.

Patient 11

The data strongly suggests the treatment was applied just as the upswingbegan. WL is very small (FIG. 17). There is more than one curve, so therating was penalized—Rating: 4.

Patient 12

Treatment was applied just before the peak of immune system activity(FIG. 18). All curves agree, and OL is small—Rating: 5.

Summary

All 12 patients exhibited oscillating CRP levels with an averageperiodicity of 7.8 days. Only 11 patients were treated (1 patient hadrapid tumor progression). The two patients who remain progression-freefor >2 years (1 PR, 1 CR), were treated in the pre-peak section of theCRP cycle, distinctly separate from the other patients treated postCRP-peak (all progressed <5 months).

An overview of predicted patient treatment times is provided in FIG. 19.Patient numbers represented above the line are assigned with high rating(confidence) (4-5), numbers below the line are assigned with lowconfidence (1-2), numbers on the line have average rating (3).

This data suggests that patient clinical outcome is dependent on thetiming of therapy relative to an individual patient's immune responsecycle and outline the dynamic equilibrium of systemic immune homeostasisin patients with advanced melanoma. This data suggests that the optimaltiming of administration of the therapy to treat cancer in relation tocycling CRP levels is at least about half-way up the rise of the CRPlevels but before they have peaked.

Example 2 Modelling to Predict Preferred Timing ofAdministration—Protocol Assessment

Introduction

The test of the software comprised two main portions: the use of thesoftware on data from real and simulated patients and a simulationstudy. The overarching goal of the algorithm is to make the predictionas accurate as possible. The inventors can assess its ability to do soin simple, easy-to-grasp cases, as a means of developing intuition abouthow it will perform in complex cases that are harder to understand.

Simulated Patients

This example provides a demonstration of the use of the fittingalgorithm on simulated patients.

Random patient were generated as follows:

-   -   >p.random←patient(id=“15 Daily Measures”, delay=5, random=TRUE,        +cv=75, parameters=c(7, 2, 2, 1), rel.days=0:14)

It will be appreciated that random patients can be generated using anysuitable statistical computing environment, such as open-sourceprogramming language R and MATLAB.

The random patient is then processed and reported using the followingcode→report(p.random). Note that each simulated patient has a light grayharmonic curve. This is the curve that was used to generate thepatient's data, so can be thought of as the “truth” that our algorithmis trying to match. The inventors experimented with the underlyingvariability until we found level that seemed consonant with thevariability observed in the data from the measured patients.

Low Variability

The inventors started with low-variability scenarios here to provide asense of how the fitting algorithm works for “ideal” patients.

FIG. 20 shows a typical scenario of six measurements spaced two daysapart. The variability and poor fit created by this design arereasonably well captured in the figure. The length of the 95% confidenceintervals of the estimated wait time (WL) is low enough for satisfactoryprediction, but the model choice is not unequivocal.

FIG. 21 shows six measurements spaced one day apart. This scenarioprovides a timely warning: in a cycle of seven days, measuring at thewrong six days can be misleading. Here, the model is uncertain of theperiod because of the errors in the measurements and the small number ofmeasurements. The length of the 95% confidence intervals of theestimated wait time (WL) is high in this case.

FIG. 22 shows ten daily measurements. The benefit of four extrameasurements is clear. The predicted curve matches the actual curvequite well.

FIG. 23 shows ten measurements spaced across two weeks, with a greaterfocus on the second week. Again the predicted curve is a good match forthe actual curve. The length of the 95% confidence interval of theestimated wait time (WL) is low enough for satisfactory prediction.

FIG. 24 shows fifteen daily measurements. Again the predicted curve is agood match for the actual curve. The length of the 95% confidenceinterval of the estimated wait time (WL) is low enough for satisfactoryprediction.

The overview from these simulations is that the algorithm performs wellfor low variability patients.

High Variability

Five high-variability scenarios here are analysed to provide a sense ofhow the fitting algorithm works for “difficult” patients.

FIG. 25 shows a typical scenario of six measurements spaced two daysapart. The variability and poor fit created by this design arereasonably well captured in the figure. The length of the 95% confidenceintervals of the estimated wait time is low, but not low enough forsatisfactory prediction.

FIG. 26 shows six measurements spaced one day apart. This scenarioprovides a timely warning: in a cycle of seven days, measuring at thewrong six days can be extremely misleading. Here, the model fails tocapture the periodicity because of the errors in the measurements andthe small number of measurements. We see warning flags in theexceptionally large estimate of the period, but nowhere else in ourdiagnostics.

FIG. 27 shows ten daily measurements. The benefit of four extrameasurements is clear. The predicted curve matches the actual curvequite well, although the importance of timeliness is also obvious.Within a few weeks of the last measurement, the estimated window oftreatment probably no longer overlaps the actual window. This is ofconcern even ignoring the possibility that the patient's immune responsecould change timing of its own accord, or in response to stimuli.

FIG. 28 shows ten measurements spaced across two weeks, with a greaterfocus on the second week. Again the predicted curve is a good match forthe actual curve. The length of the 95% confidence interval of theestimated wait time is low, but not quite low enough for satisfactoryprediction.

FIG. 29 shows fifteen daily measurements. One of the curves has missedthe pattern altogether, but if we follow our algorithm then theprediction from this curve would not be used anyway. The overview fromthe high-variability random patients is less encouraging, which is anexpected result. As the underlying variability of the signal increases,we are able to rely less on the data to inform us about the nature ofthe true signal.

Conclusions

The results suggest that the proposed strategy is defensibly robust andworks under a wide range of different circumstances. However, care isrequired in its application, and datasets of reasonable size (e.g. atleast 10) will yield better results. If the sample size is too smallthen the confidence with which the technique identifies the location ofthe treatment window will be overstated.

Example 3 Simulation Study

Materials and Methods

The present inventors used the model and fitting algorithm as laid outin Example 1. The goal was to assess the impact upon predictionperformance of the number of measures taken, the timeframe over whichthey were taken, and the pattern of spacing. It is reasonable to expectthat the underlying variability of the patients biological signal wouldalso affect the quality of the model fit. Therefore the design for thesimulation study comprised the following elements:

1. Variation in length, including one, one and a half, and two weeks;

2. Variation in number of measurements, including 8, 10, 15, and 21;

3. Variation in measurement pattern, including symmetric (S),concentration early and late (B), and concentration late (L); and

4. Variation in underlying patient variability, including very small(0.25%) and nominal CRP variation (4%) to large (30%).

The inventors simulated 500 random patients with each of the threeunderlying amounts of variability, crossed with each differentmeasurement scenario. A full factorial experimental design was not usedowing to time constraints. Each random patient was fitted using thesuggested algorithm. For each patient we then assessed the length oftheir confidence interval and whether or not the interval contained thetrue value from which the patient had been simulated.

Results

The full results of the simulations are presented in Table 1, and thespecific results for interval length are summarized in FIG. 30. Theresults for interval coverage rates are summarized in FIG. 31.

Table 1

Results from simulations. The Variability refers to the coefficient ofvariation of the data; Count is the number of measurements; Span is thenumber of days over which the measurements were made; Symmetry refers tothe distribution of the measurements across the days, including(S)ymmetric, (L)ate-focused, and (B)oth early and late; Coverage is thesimulated coverage probability (nominally 0.95); and Length is theaverage length of the intervals, in days.

Variability Count Span Symmetry Coverage Length 0.25 8 8 S 0.714 0.00330.25 8 15 S 0.742 0.0027 0.25 15 15 S 0.844 0.0029 0.25 10 15 L 0.7800.0026 0.25 10 15 B 0.810 0.0033 20.00 8 8 S 0.678 0.2600 20.00 8 15 S0.676 0.2200 20.00 15 15 S 0.848 0.2500 20.00 15 10 S 0.848 0.4900 20.0015 8 S 0.874 0.2900 20.00 22 8 S 0.888 0.2700 20.00 22 15 S 0.906 0.230020.00 15 10 L 0.860 0.3400 20.00 22 15 L 0.900 0.2100 35.00 8 8 S 0.7200.5700 35.00 8 15 S 0.750 0.4400 35.00 15 15 S 0.870 0.4300 35.00 10 15L 0.800 0.5000 35.00 10 15 B 0.790 0.5100Discussion and Conclusion

Based upon the patient data that were available, the nominal figure of4% variation in CRP measurements seems very low. It is also possiblethat the figure is correct but our model fails to capture some importantsource of variation. This study suggests that the proposed modelingtechnique works better with at least moderate numbers of data points,say at least 10. The arrangement of the measurement points and the spanof time that they occupy does not seem to affect the outcome, at leastacross the range of scenarios compared here. The underlying variabilityof the measurements does affect the outcome, and efforts should be madeto ensure that measurements are made in as uniform a collection ofcircumstances as is possible.

The goals of the simulation study were two-fold: firstly to provideguidance as to the most suitable measurement timing regime; andsecondly, to provide feedback on the reliability of the fitting routine.The second goal is addressed here.

The measure of reliability that is focused on is the realized coveragerate of the random intervals. The nominal coverage rate is 0.95, andcloseness to this coverage should be regarded as one measure of thequality of the fitting approach. However, with the small sample sizesthat we are using, and the nature of the model being fitted, it would bevery surprising to achieve coverage rates that high. Furthermore, it isnot essential that the intervals achieve any particular coverage ratebecause they are being used an informal way, to provide feedback as tothe reliability of the data and model, rather than as a formalinferential tool.

The coverage rates and interval lengths from the simulation study arereported in Table 1. The simulation study suggests that the underlyingvariability of the signal does not greatly affect the quality of theintervals, as measured by the closeness of the coverage rate to 0.95.The coverage increases with increasing sample size, seems largelyunaffected by the span of the measurement period, symmetry andunderlying variability (FIG. 31).

The inventors conclude that for sufficiently large numbers ofobservations, say 10 or more, the coverage rate is reasonably good forthe fitting technique. This provides reasonable confidence for thetechnique itself, as well as the use of the intervals as diagnostictools for modeling with real patients.

Samples that are too small will have overstated coverage, which meansthat the intervals are shorter than imagined. This means that the resultwill overstate the confidence with which we can identify the location ofthe treatment window.

It will be appreciated by persons skilled in the art that numerousvariations and/or modifications may be made to the invention as shown inthe specific embodiments without departing from the spirit or scope ofthe invention as broadly described. The present embodiments are,therefore, to be considered in all respects as illustrative and notrestrictive.

The present application claims priority from U.S. 61/181,508 filed 27May 2009, the entire contents of which are incorporated herein byreference.

All publications discussed above are incorporated herein in theirentirety.

Any discussion of documents, acts, materials, devices, articles or thelike which has been included in the present specification is solely forthe purpose of providing a context for the present invention. It is notto be taken as an admission that any or all of these matters form partof the prior art base or were common general knowledge in the fieldrelevant to the present invention as it existed before the priority dateof each claim of this application.

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The invention claimed is:
 1. A method of treating a disease in a subjectin which the immune system is cycling, the method comprising;administering a therapy to the subject at a preferred time in thefuture, wherein the preferred time in the future has been determinedusing a computer-implemented method for analysing a biomarker whichcycles in the subject, the method comprising estimating a periodicity ofthe cycling of the biomarker based on measurements of the biomarker insamples from the subject by obtaining a best-fit curve of themeasurements in accordance with a model of the cycling of the biomarkerand determining from the estimated periodicity of the cycling apreferred time in the future to administer the therapy to treat thedisease in the subject by projecting the obtained best-fit curve intothe future, and wherein the disease is characterized by the productionof regulator T cells and cycling immune system markers.
 2. A method oftreating a disease characterized by production of regulator cells andcycling immune system markers in a subject, the method comprising: i)using a computer-implemented method for analysing a biomarker whichcycles in the subject to estimate a periodicity of the cycling of thebiomarker based on measurements of the biomarker in samples from thesubject by obtaining a best-fit curve of the measurements in accordancewith a model of the cycling of the biomarker; ii) determining from theestimated periodicity of the cycling a preferred time in the future toadminister the therapy to treat the disease in the subject by projectingthe obtained best-fit curve into the future, and ii) administering thetherapy at the preferred time.
 3. The method of claim 1, wherein thebest-fit curve is a fit to a harmonic model of the cycling of thebiomarker.
 4. The method of claim 3, wherein obtaining the best-fitcurve further comprises fitting the harmonic model to the measurementsmultiple times using different initial conditions for each fit, andgenerating a confidence measure by comparing a similarity in a fitresult produced by each fitting.
 5. The method of claim 4, wherein theharmonic model has a heavy tail to allow for noisy nature of themeasurements.
 6. The method of claim 1, wherein obtaining the best curvefit comprises fitting a plurality of different regression models to themeasurements.
 7. The method of claim 1, wherein obtaining the best-fitcurve further comprises imposing a box constraint on at least someparameters of the model to guide optimisation to biologically realisticregions.
 8. The method of claim 1 which further comprises determining avariance-stabilizing transformation of the measurements prior toestimating the periodicity based on the transformed measurements.
 9. Themethod of claim 1, wherein determining the preferred time to administerthe therapy comprises comparing the measurements of the subject with adatabase of measurements of the biomarker obtained from other subjects.10. The method of claim 1, wherein the measurements are for two or moredifferent biomarkers which are cycling in the subject, and determiningthe preferred time to administer the therapy is based on the two or moresets of measurements.
 11. The method of claim 1, wherein the biomarkeris an acute phase inflammatory marker.
 12. The method of claim 1,wherein the therapy is selected from the group consisting of ananti-cancer drug, a vaccine, radiation, dsRNA and an antibody whichinhibit the production and/or activity of regulator T cells.
 13. Themethod of claim 12, wherein the anti-cancer drug is ananti-proliferative drug.
 14. The method of claim 1, wherein the diseaseis cancer.
 15. The method of claim 2, wherein the best-fit curve is afit to a harmonic model of the cycling of the biomarker.
 16. The methodof claim 2, wherein obtaining the best curve fit comprises fitting aplurality of different regression models to the measurements.
 17. Themethod of claim 2, wherein obtaining the best-fit curve furthercomprises imposing a box constraint on at least some parameters of themodel to guide optimisation to biologically realistic regions.
 18. Themethod of claim 2 which further comprises determining avariance-stabilizing transformation of the measurements prior toestimating the periodicity based on the transformed measurements. 19.The method of claim 2, wherein determining the preferred time toadminister the therapy comprises comparing the measurements of thesubject with a database of measurements of the biomarker obtained fromother subjects.
 20. The method of claim 2, wherein the measurements arefor two or more different biomarkers which are cycling in the subject,and determining the preferred time to administer the therapy is based onthe two or more sets of measurements.